爵士乐生成

栏目: 数据库 · 发布时间: 5年前

内容简介：这是序列模型课程第一周的编程作业 Part 3Welcome to your final programming assignment of this week! In this notebook, you will implement a model that uses an LSTM to generate music. You will even be able to listen to your own music at the end of the assignment.Please run th

这是序列模型课程第一周的编程作业 Part 3

Improvise a Jazz Solo with an LSTM Network

Welcome to your final programming assignment of this week! In this notebook, you will implement a model that uses an LSTM to generate music. You will even be able to listen to your own music at the end of the assignment.

You will learn to:

Apply an LSTM to music generation.
Generate your own jazz music with deep learning.

Please run the following cell to load all the packages required in this assignment. This may take a few minutes.

from __future__ import print_function
import IPython
import sys
from music21 import *
import numpy as np
from grammar import *
from qa import *
from preprocess import * 
from music_utils import *
from data_utils import *
from keras.models import load_model, Model
from keras.layers import Dense, Activation, Dropout, Input, LSTM, Reshape, Lambda, RepeatVector
from keras.initializers import glorot_uniform
from keras.utils import to_categorical
from keras.optimizers import Adam
from keras import backend as K

1 - Problem statement

You would like to create a jazz music piece specially for a friend’s birthday. However, you don’t know any instruments or music composition. Fortunately, you know deep learning and will solve this problem using an LSTM netwok.

You will train a network to generate novel jazz solos in a style representative of a body of performed work.

爵士乐生成

1.1 - Dataset

You will train your algorithm on a corpus of Jazz music. Run the cell below to listen to a snippet of the audio from the training set:

IPython.display.Audio('./data/30s_seq.mp3')

We have taken care of the preprocessing of the musical data to render it in terms of musical “values.” You can informally think of each “value” as a note, which comprises a pitch and a duration. For example, if you press down a specific piano key for 0.5 seconds, then you have just played a note. In music theory, a “value” is actually more complicated than this–specifically, it also captures the information needed to play multiple notes at the same time. For example, when playing a music piece, you might press down two piano keys at the same time (playng multiple notes at the same time generates what’s called a “chord”). But we don’t need to worry about the details of music theory for this assignment. For the purpose of this assignment, all you need to know is that we will obtain a dataset of values, and will learn an RNN model to generate sequences of values.

Our music generation system will use 78 unique values. Run the following code to load the raw music data and preprocess it into values. This might take a few minutes.

X, Y, n_values, indices_values = load_music_utils()
print('shape of X:', X.shape)
print('number of training examples:', X.shape[0])
print('Tx (length of sequence):', X.shape[1])
print('total # of unique values:', n_values)
print('Shape of Y:', Y.shape)

shape of X: (60, 30, 78)
number of training examples: 60
Tx (length of sequence): 30
total # of unique values: 78
Shape of Y: (30, 60, 78)

You have just loaded the following:

X : This is an (m, $T_x$, 78) dimensional array. We have m training examples, each of which is a snippet of $T_x =30$ musical values. At each time step, the input is one of 78 different possible values, represented as a one-hot vector. Thus for example, X[i,t,:] is a one-hot vector representating the value of the i-th example at time t.
Y : This is essentially the same as X , but shifted one step to the left (to the past). Similar to the dinosaurus assignment, we’re interested in the network using the previous values to predict the next value, so our sequence model will try to predict $y^{\langle t \rangle}$ given $x^{\langle 1\rangle}, \ldots, x^{\langle t \rangle}$. However, the data in Y is reordered to be dimension $(T_y, m, 78)$, where $T_y = T_x$. This format makes it more convenient to feed to the LSTM later.
n_values : The number of unique values in this dataset. This should be 78.
indices_values : python dictionary mapping from 0-77 to musical values.

1.2 - Overview of our model

Here is the architecture of the model we will use. This is similar to the Dinosaurus model you had used in the previous notebook, except that in you will be implementing it in Keras. The architecture is as follows:

爵士乐生成

We will be training the model on random snippets of 30 values taken from a much longer piece of music. Thus, we won’t bother to set the first input $x^{\langle 1 \rangle} = \vec{0}$, which we had done previously to denote the start of a dinosaur name, since now most of these snippets of audio start somewhere in the middle of a piece of music. We are setting each of the snippts to have the same length $T_x = 30$ to make vectorization easier.

2 - Building the model

In this part you will build and train a model that will learn musical patterns. To do so, you will need to build a model that takes in X of shape $(m, T_x, 78)$ and Y of shape $(T_y, m, 78)$. We will use an LSTM with 64 dimensional hidden states. Lets set n_a = 64 .

n_a = 64

Here’s how you can create a Keras model with multiple inputs and outputs. If you’re building an RNN where even at test time entire input sequence $x^{\langle 1 \rangle}, x^{\langle 2 \rangle}, \ldots, x^{\langle T_x \rangle}$ were given in advance , for example if the inputs were words and the output was a label, then Keras has simple built-in functions to build the model. However, for sequence generation, at test time we don’t know all the values of $x^{\langle t\rangle}$ in advance; instead we generate them one at a time using $x^{\langle t\rangle} = y^{\langle t-1 \rangle}$. So the code will be a bit more complicated, and you’ll need to implement your own for-loop to iterate over the different time steps.

The function djmodel() will call the LSTM layer $T_x$ times using a for-loop, and it is important that all $T_x$ copies have the same weights. I.e., it should not re-initiaiize the weights every time—the $T_x$ steps should have shared weights. The key steps for implementing layers with shareable weights in Keras are:

Define the layer objects (we will use global variables for this).
Call these objects when propagating the input.

We have defined the layers objects you need as global variables. Please run the next cell to create them. Please check the Keras documentation to make sure you understand what these layers are: Reshape() , LSTM() , Dense() .

reshapor = Reshape((1, 78))                        # Used in Step 2.B of djmodel(), below
LSTM_cell = LSTM(n_a, return_state = True)         # Used in Step 2.C
densor = Dense(n_values, activation='softmax')     # Used in Step 2.D

Each of reshapor , LSTM_cell and densor are now layer objects, and you can use them to implement djmodel() . In order to propagate a Keras tensor object X through one of these layers, use layer_object(X) (or layer_object([X,Y]) if it requires multiple inputs.). For example, reshapor(X) will propagate X through the Reshape((1,78)) layer defined above.

Exercise: Implement djmodel() . You will need to carry out 2 steps:

Create an empty list “outputs” to save the outputs of the LSTM Cell at every time step.

Loop for $t \in 1, \ldots, T_x$:

A. Select the “t”th time-step vector from X. The shape of this selection should be (78,). To do so, create a custom Lambda layer in Keras by using this line of code:

           x = Lambda(lambda x: X[:,t,:])(X)
``` 
Look over the Keras documentation to figure out what this does. It is creating a "temporary" or "unnamed" function (that's what Lambda functions are) that extracts out the appropriate one-hot vector, and making this function a Keras `Layer` object to apply to `X`. 

    B. Reshape x to be (1,78). You may find the `reshapor()` layer (defined below) helpful.

    C. Run x through one step of LSTM_cell. Remember to initialize the LSTM_cell with the previous step's hidden state $a$ and cell state $c$. Use the following formatting:
```python
a, _, c = LSTM_cell(input_x, initial_state=[previous hidden state, previous cell state])

D. Propagate the LSTM’s output activation value through a dense+softmax layer using densor .

E. Append the predicted value to the list of “outputs”

# GRADED FUNCTION: djmodel

def djmodel(Tx, n_a, n_values):
    """
Implement the model

Arguments:
Tx -- length of the sequence in a corpus
n_a -- the number of activations used in our model
n_values -- number of unique values in the music data

Returns:
model -- a keras model with the
"""
    
    # Define the input of your model with a shape
    X = Input(shape=(Tx, n_values))
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    
    ### START CODE HERE ###
    # Step 1: Create empty list to append the outputs while you iterate (≈1 line)
    outputs = list()
    
    # Step 2: Loop
    for t in range(Tx):
        
        # Step 2.A: select the "t"th time step vector from X.
        x = Lambda(lambda x: X[:,t,:])(X)    # x.shape=(?,78)
        # Step 2.B: Use reshapor to reshape x to be (1, n_values) (≈1 line)
        x = reshapor(x)    # x.shape=(?,1,78)
        # Step 2.C: Perform one step of the LSTM_cell
        a, _, c = LSTM_cell(x, initial_state=[a, c])
        # Step 2.D: Apply densor to the hidden state output of LSTM_Cell
        out = densor(a)    # out.shape=(?,78)
        # Step 2.E: add the output to "outputs"
        outputs.append(out)
        
    # Step 3: Create model instance
    model = Model(inputs=[X, a0, c0], outputs=outputs)
    
    ### END CODE HERE ###
    
    return model

Run the following cell to define your model. We will use Tx=30 , n_a=64 (the dimension of the LSTM activations), and n_values=78 . This cell may take a few seconds to run.

model = djmodel(Tx = 30 , n_a = 64, n_values = 78)

You now need to compile your model to be trained. We will Adam and a categorical cross-entropy loss.

opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, decay=0.01)

model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])

Finally, lets initialize a0 and c0 for the LSTM’s initial state to be zero.

m = 60
a0 = np.zeros((m, n_a))
c0 = np.zeros((m, n_a))

Lets now fit the model! We will turn Y to a list before doing so, since the cost function expects Y to be provided in this format (one list item per time-step). So list(Y) is a list with 30 items, where each of the list items is of shape (60,78). Lets train for 100 epochs. This will take a few minutes.

model.fit([X, a0, c0], list(Y), epochs=100)

Epoch 1/100
60/60 [==============================] - 1s - loss: 5.8591 - dense_3_loss_1: 3.7364 - dense_3_loss_2: 1.1701 - dense_3_loss_3: 0.2851 - dense_3_loss_4: 0.0804 - dense_3_loss_5: 0.0442 - dense_3_loss_6: 0.0381 - dense_3_loss_7: 0.0290 - dense_3_loss_8: 0.0269 - dense_3_loss_9: 0.0232 - dense_3_loss_10: 0.0201 - dense_3_loss_11: 0.0214 - dense_3_loss_12: 0.0211 - dense_3_loss_13: 0.0182 - dense_3_loss_14: 0.0194 - dense_3_loss_15: 0.0205 - dense_3_loss_16: 0.0209 - dense_3_loss_17: 0.0196 - dense_3_loss_18: 0.0195 - dense_3_loss_19: 0.0209 - dense_3_loss_20: 0.0222 - dense_3_loss_21: 0.0229 - dense_3_loss_22: 0.0209 - dense_3_loss_23: 0.0195 - dense_3_loss_24: 0.0193 - dense_3_loss_25: 0.0207 - dense_3_loss_26: 0.0206 - dense_3_loss_27: 0.0231 - dense_3_loss_28: 0.0245 - dense_3_loss_29: 0.0306 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6500 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 2/100
60/60 [==============================] - 1s - loss: 5.8304 - dense_3_loss_1: 3.7337 - dense_3_loss_2: 1.1604 - dense_3_loss_3: 0.2808 - dense_3_loss_4: 0.0790 - dense_3_loss_5: 0.0435 - dense_3_loss_6: 0.0374 - dense_3_loss_7: 0.0285 - dense_3_loss_8: 0.0264 - dense_3_loss_9: 0.0228 - dense_3_loss_10: 0.0197 - dense_3_loss_11: 0.0210 - dense_3_loss_12: 0.0207 - dense_3_loss_13: 0.0179 - dense_3_loss_14: 0.0191 - dense_3_loss_15: 0.0202 - dense_3_loss_16: 0.0205 - dense_3_loss_17: 0.0192 - dense_3_loss_18: 0.0191 - dense_3_loss_19: 0.0206 - dense_3_loss_20: 0.0218 - dense_3_loss_21: 0.0225 - dense_3_loss_22: 0.0205 - dense_3_loss_23: 0.0191 - dense_3_loss_24: 0.0189 - dense_3_loss_25: 0.0203 - dense_3_loss_26: 0.0202 - dense_3_loss_27: 0.0226 - dense_3_loss_28: 0.0240 - dense_3_loss_29: 0.0299 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6500 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 3/100
60/60 [==============================] - 1s - loss: 5.8035 - dense_3_loss_1: 3.7308 - dense_3_loss_2: 1.1517 - dense_3_loss_3: 0.2767 - dense_3_loss_4: 0.0779 - dense_3_loss_5: 0.0428 - dense_3_loss_6: 0.0368 - dense_3_loss_7: 0.0280 - dense_3_loss_8: 0.0260 - dense_3_loss_9: 0.0223 - dense_3_loss_10: 0.0194 - dense_3_loss_11: 0.0206 - dense_3_loss_12: 0.0203 - dense_3_loss_13: 0.0175 - dense_3_loss_14: 0.0187 - dense_3_loss_15: 0.0198 - dense_3_loss_16: 0.0202 - dense_3_loss_17: 0.0189 - dense_3_loss_18: 0.0188 - dense_3_loss_19: 0.0202 - dense_3_loss_20: 0.0214 - dense_3_loss_21: 0.0221 - dense_3_loss_22: 0.0202 - dense_3_loss_23: 0.0188 - dense_3_loss_24: 0.0186 - dense_3_loss_25: 0.0199 - dense_3_loss_26: 0.0199 - dense_3_loss_27: 0.0223 - dense_3_loss_28: 0.0236 - dense_3_loss_29: 0.0293 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6500 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 4/100
60/60 [==============================] - 1s - loss: 5.7779 - dense_3_loss_1: 3.7280 - dense_3_loss_2: 1.1431 - dense_3_loss_3: 0.2733 - dense_3_loss_4: 0.0766 - dense_3_loss_5: 0.0422 - dense_3_loss_6: 0.0362 - dense_3_loss_7: 0.0275 - dense_3_loss_8: 0.0255 - dense_3_loss_9: 0.0219 - dense_3_loss_10: 0.0191 - dense_3_loss_11: 0.0202 - dense_3_loss_12: 0.0200 - dense_3_loss_13: 0.0173 - dense_3_loss_14: 0.0184 - dense_3_loss_15: 0.0195 - dense_3_loss_16: 0.0198 - dense_3_loss_17: 0.0186 - dense_3_loss_18: 0.0185 - dense_3_loss_19: 0.0199 - dense_3_loss_20: 0.0210 - dense_3_loss_21: 0.0217 - dense_3_loss_22: 0.0198 - dense_3_loss_23: 0.0185 - dense_3_loss_24: 0.0183 - dense_3_loss_25: 0.0196 - dense_3_loss_26: 0.0195 - dense_3_loss_27: 0.0219 - dense_3_loss_28: 0.0232 - dense_3_loss_29: 0.0288 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6500 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 5/100
60/60 [==============================] - 1s - loss: 5.7514 - dense_3_loss_1: 3.7254 - dense_3_loss_2: 1.1346 - dense_3_loss_3: 0.2688 - dense_3_loss_4: 0.0754 - dense_3_loss_5: 0.0416 - dense_3_loss_6: 0.0356 - dense_3_loss_7: 0.0270 - dense_3_loss_8: 0.0251 - dense_3_loss_9: 0.0216 - dense_3_loss_10: 0.0187 - dense_3_loss_11: 0.0199 - dense_3_loss_12: 0.0196 - dense_3_loss_13: 0.0170 - dense_3_loss_14: 0.0181 - dense_3_loss_15: 0.0192 - dense_3_loss_16: 0.0195 - dense_3_loss_17: 0.0182 - dense_3_loss_18: 0.0182 - dense_3_loss_19: 0.0195 - dense_3_loss_20: 0.0206 - dense_3_loss_21: 0.0213 - dense_3_loss_22: 0.0195 - dense_3_loss_23: 0.0182 - dense_3_loss_24: 0.0180 - dense_3_loss_25: 0.0192 - dense_3_loss_26: 0.0192 - dense_3_loss_27: 0.0215 - dense_3_loss_28: 0.0227 - dense_3_loss_29: 0.0282 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6500 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 6/100
60/60 [==============================] - 1s - loss: 5.7255 - dense_3_loss_1: 3.7227 - dense_3_loss_2: 1.1255 - dense_3_loss_3: 0.2650 - dense_3_loss_4: 0.0741 - dense_3_loss_5: 0.0409 - dense_3_loss_6: 0.0350 - dense_3_loss_7: 0.0266 - dense_3_loss_8: 0.0247 - dense_3_loss_9: 0.0212 - dense_3_loss_10: 0.0184 - dense_3_loss_11: 0.0195 - dense_3_loss_12: 0.0193 - dense_3_loss_13: 0.0167 - dense_3_loss_14: 0.0178 - dense_3_loss_15: 0.0189 - dense_3_loss_16: 0.0192 - dense_3_loss_17: 0.0179 - dense_3_loss_18: 0.0179 - dense_3_loss_19: 0.0192 - dense_3_loss_20: 0.0203 - dense_3_loss_21: 0.0209 - dense_3_loss_22: 0.0192 - dense_3_loss_23: 0.0179 - dense_3_loss_24: 0.0177 - dense_3_loss_25: 0.0189 - dense_3_loss_26: 0.0189 - dense_3_loss_27: 0.0212 - dense_3_loss_28: 0.0224 - dense_3_loss_29: 0.0277 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6667 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 7/100
60/60 [==============================] - 1s - loss: 5.7018 - dense_3_loss_1: 3.7200 - dense_3_loss_2: 1.1176 - dense_3_loss_3: 0.2616 - dense_3_loss_4: 0.0731 - dense_3_loss_5: 0.0404 - dense_3_loss_6: 0.0344 - dense_3_loss_7: 0.0262 - dense_3_loss_8: 0.0243 - dense_3_loss_9: 0.0208 - dense_3_loss_10: 0.0181 - dense_3_loss_11: 0.0192 - dense_3_loss_12: 0.0189 - dense_3_loss_13: 0.0164 - dense_3_loss_14: 0.0175 - dense_3_loss_15: 0.0185 - dense_3_loss_16: 0.0189 - dense_3_loss_17: 0.0176 - dense_3_loss_18: 0.0176 - dense_3_loss_19: 0.0189 - dense_3_loss_20: 0.0199 - dense_3_loss_21: 0.0206 - dense_3_loss_22: 0.0189 - dense_3_loss_23: 0.0175 - dense_3_loss_24: 0.0174 - dense_3_loss_25: 0.0186 - dense_3_loss_26: 0.0185 - dense_3_loss_27: 0.0208 - dense_3_loss_28: 0.0220 - dense_3_loss_29: 0.0272 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 8/100
60/60 [==============================] - 1s - loss: 5.6775 - dense_3_loss_1: 3.7175 - dense_3_loss_2: 1.1091 - dense_3_loss_3: 0.2583 - dense_3_loss_4: 0.0720 - dense_3_loss_5: 0.0398 - dense_3_loss_6: 0.0338 - dense_3_loss_7: 0.0258 - dense_3_loss_8: 0.0239 - dense_3_loss_9: 0.0205 - dense_3_loss_10: 0.0178 - dense_3_loss_11: 0.0189 - dense_3_loss_12: 0.0186 - dense_3_loss_13: 0.0161 - dense_3_loss_14: 0.0172 - dense_3_loss_15: 0.0182 - dense_3_loss_16: 0.0186 - dense_3_loss_17: 0.0173 - dense_3_loss_18: 0.0173 - dense_3_loss_19: 0.0186 - dense_3_loss_20: 0.0196 - dense_3_loss_21: 0.0202 - dense_3_loss_22: 0.0186 - dense_3_loss_23: 0.0172 - dense_3_loss_24: 0.0171 - dense_3_loss_25: 0.0183 - dense_3_loss_26: 0.0182 - dense_3_loss_27: 0.0205 - dense_3_loss_28: 0.0216 - dense_3_loss_29: 0.0267 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 9/100
60/60 [==============================] - 1s - loss: 5.6535 - dense_3_loss_1: 3.7149 - dense_3_loss_2: 1.1006 - dense_3_loss_3: 0.2545 - dense_3_loss_4: 0.0710 - dense_3_loss_5: 0.0392 - dense_3_loss_6: 0.0332 - dense_3_loss_7: 0.0254 - dense_3_loss_8: 0.0235 - dense_3_loss_9: 0.0202 - dense_3_loss_10: 0.0175 - dense_3_loss_11: 0.0186 - dense_3_loss_12: 0.0183 - dense_3_loss_13: 0.0159 - dense_3_loss_14: 0.0170 - dense_3_loss_15: 0.0180 - dense_3_loss_16: 0.0183 - dense_3_loss_17: 0.0171 - dense_3_loss_18: 0.0170 - dense_3_loss_19: 0.0182 - dense_3_loss_20: 0.0193 - dense_3_loss_21: 0.0199 - dense_3_loss_22: 0.0183 - dense_3_loss_23: 0.0169 - dense_3_loss_24: 0.0169 - dense_3_loss_25: 0.0180 - dense_3_loss_26: 0.0179 - dense_3_loss_27: 0.0201 - dense_3_loss_28: 0.0213 - dense_3_loss_29: 0.0263 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 10/100
60/60 [==============================] - 1s - loss: 5.6313 - dense_3_loss_1: 3.7121 - dense_3_loss_2: 1.0935 - dense_3_loss_3: 0.2514 - dense_3_loss_4: 0.0701 - dense_3_loss_5: 0.0386 - dense_3_loss_6: 0.0328 - dense_3_loss_7: 0.0251 - dense_3_loss_8: 0.0231 - dense_3_loss_9: 0.0199 - dense_3_loss_10: 0.0173 - dense_3_loss_11: 0.0184 - dense_3_loss_12: 0.0180 - dense_3_loss_13: 0.0156 - dense_3_loss_14: 0.0167 - dense_3_loss_15: 0.0177 - dense_3_loss_16: 0.0180 - dense_3_loss_17: 0.0168 - dense_3_loss_18: 0.0167 - dense_3_loss_19: 0.0180 - dense_3_loss_20: 0.0190 - dense_3_loss_21: 0.0196 - dense_3_loss_22: 0.0180 - dense_3_loss_23: 0.0166 - dense_3_loss_24: 0.0166 - dense_3_loss_25: 0.0177 - dense_3_loss_26: 0.0176 - dense_3_loss_27: 0.0198 - dense_3_loss_28: 0.0209 - dense_3_loss_29: 0.0258 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 11/100
60/60 [==============================] - 1s - loss: 5.6094 - dense_3_loss_1: 3.7095 - dense_3_loss_2: 1.0854 - dense_3_loss_3: 0.2489 - dense_3_loss_4: 0.0691 - dense_3_loss_5: 0.0381 - dense_3_loss_6: 0.0323 - dense_3_loss_7: 0.0247 - dense_3_loss_8: 0.0228 - dense_3_loss_9: 0.0196 - dense_3_loss_10: 0.0170 - dense_3_loss_11: 0.0181 - dense_3_loss_12: 0.0177 - dense_3_loss_13: 0.0154 - dense_3_loss_14: 0.0164 - dense_3_loss_15: 0.0174 - dense_3_loss_16: 0.0178 - dense_3_loss_17: 0.0165 - dense_3_loss_18: 0.0165 - dense_3_loss_19: 0.0177 - dense_3_loss_20: 0.0187 - dense_3_loss_21: 0.0193 - dense_3_loss_22: 0.0177 - dense_3_loss_23: 0.0164 - dense_3_loss_24: 0.0164 - dense_3_loss_25: 0.0174 - dense_3_loss_26: 0.0173 - dense_3_loss_27: 0.0195 - dense_3_loss_28: 0.0206 - dense_3_loss_29: 0.0254 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 12/100
60/60 [==============================] - 1s - loss: 5.5874 - dense_3_loss_1: 3.7069 - dense_3_loss_2: 1.0780 - dense_3_loss_3: 0.2454 - dense_3_loss_4: 0.0681 - dense_3_loss_5: 0.0375 - dense_3_loss_6: 0.0319 - dense_3_loss_7: 0.0243 - dense_3_loss_8: 0.0224 - dense_3_loss_9: 0.0193 - dense_3_loss_10: 0.0168 - dense_3_loss_11: 0.0178 - dense_3_loss_12: 0.0174 - dense_3_loss_13: 0.0151 - dense_3_loss_14: 0.0162 - dense_3_loss_15: 0.0171 - dense_3_loss_16: 0.0175 - dense_3_loss_17: 0.0163 - dense_3_loss_18: 0.0162 - dense_3_loss_19: 0.0175 - dense_3_loss_20: 0.0184 - dense_3_loss_21: 0.0190 - dense_3_loss_22: 0.0174 - dense_3_loss_23: 0.0161 - dense_3_loss_24: 0.0161 - dense_3_loss_25: 0.0172 - dense_3_loss_26: 0.0171 - dense_3_loss_27: 0.0192 - dense_3_loss_28: 0.0203 - dense_3_loss_29: 0.0250 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 13/100
60/60 [==============================] - 1s - loss: 5.5655 - dense_3_loss_1: 3.7045 - dense_3_loss_2: 1.0698 - dense_3_loss_3: 0.2427 - dense_3_loss_4: 0.0670 - dense_3_loss_5: 0.0370 - dense_3_loss_6: 0.0313 - dense_3_loss_7: 0.0239 - dense_3_loss_8: 0.0221 - dense_3_loss_9: 0.0190 - dense_3_loss_10: 0.0165 - dense_3_loss_11: 0.0176 - dense_3_loss_12: 0.0171 - dense_3_loss_13: 0.0149 - dense_3_loss_14: 0.0159 - dense_3_loss_15: 0.0168 - dense_3_loss_16: 0.0172 - dense_3_loss_17: 0.0160 - dense_3_loss_18: 0.0160 - dense_3_loss_19: 0.0172 - dense_3_loss_20: 0.0180 - dense_3_loss_21: 0.0187 - dense_3_loss_22: 0.0171 - dense_3_loss_23: 0.0159 - dense_3_loss_24: 0.0159 - dense_3_loss_25: 0.0169 - dense_3_loss_26: 0.0168 - dense_3_loss_27: 0.0189 - dense_3_loss_28: 0.0199 - dense_3_loss_29: 0.0245 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 14/100
60/60 [==============================] - 1s - loss: 5.5450 - dense_3_loss_1: 3.7020 - dense_3_loss_2: 1.0625 - dense_3_loss_3: 0.2400 - dense_3_loss_4: 0.0662 - dense_3_loss_5: 0.0366 - dense_3_loss_6: 0.0309 - dense_3_loss_7: 0.0236 - dense_3_loss_8: 0.0217 - dense_3_loss_9: 0.0187 - dense_3_loss_10: 0.0163 - dense_3_loss_11: 0.0173 - dense_3_loss_12: 0.0169 - dense_3_loss_13: 0.0147 - dense_3_loss_14: 0.0157 - dense_3_loss_15: 0.0166 - dense_3_loss_16: 0.0170 - dense_3_loss_17: 0.0157 - dense_3_loss_18: 0.0157 - dense_3_loss_19: 0.0169 - dense_3_loss_20: 0.0178 - dense_3_loss_21: 0.0184 - dense_3_loss_22: 0.0169 - dense_3_loss_23: 0.0157 - dense_3_loss_24: 0.0156 - dense_3_loss_25: 0.0166 - dense_3_loss_26: 0.0166 - dense_3_loss_27: 0.0186 - dense_3_loss_28: 0.0196 - dense_3_loss_29: 0.0242 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 15/100
60/60 [==============================] - 1s - loss: 5.5244 - dense_3_loss_1: 3.6995 - dense_3_loss_2: 1.0552 - dense_3_loss_3: 0.2371 - dense_3_loss_4: 0.0653 - dense_3_loss_5: 0.0360 - dense_3_loss_6: 0.0305 - dense_3_loss_7: 0.0233 - dense_3_loss_8: 0.0214 - dense_3_loss_9: 0.0185 - dense_3_loss_10: 0.0160 - dense_3_loss_11: 0.0171 - dense_3_loss_12: 0.0166 - dense_3_loss_13: 0.0145 - dense_3_loss_14: 0.0154 - dense_3_loss_15: 0.0163 - dense_3_loss_16: 0.0168 - dense_3_loss_17: 0.0155 - dense_3_loss_18: 0.0155 - dense_3_loss_19: 0.0167 - dense_3_loss_20: 0.0175 - dense_3_loss_21: 0.0181 - dense_3_loss_22: 0.0167 - dense_3_loss_23: 0.0154 - dense_3_loss_24: 0.0154 - dense_3_loss_25: 0.0164 - dense_3_loss_26: 0.0163 - dense_3_loss_27: 0.0184 - dense_3_loss_28: 0.0193 - dense_3_loss_29: 0.0238 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 16/100
60/60 [==============================] - 1s - loss: 5.5047 - dense_3_loss_1: 3.6970 - dense_3_loss_2: 1.0483 - dense_3_loss_3: 0.2344 - dense_3_loss_4: 0.0646 - dense_3_loss_5: 0.0356 - dense_3_loss_6: 0.0301 - dense_3_loss_7: 0.0230 - dense_3_loss_8: 0.0211 - dense_3_loss_9: 0.0182 - dense_3_loss_10: 0.0158 - dense_3_loss_11: 0.0168 - dense_3_loss_12: 0.0164 - dense_3_loss_13: 0.0142 - dense_3_loss_14: 0.0152 - dense_3_loss_15: 0.0161 - dense_3_loss_16: 0.0165 - dense_3_loss_17: 0.0153 - dense_3_loss_18: 0.0153 - dense_3_loss_19: 0.0164 - dense_3_loss_20: 0.0173 - dense_3_loss_21: 0.0179 - dense_3_loss_22: 0.0164 - dense_3_loss_23: 0.0152 - dense_3_loss_24: 0.0152 - dense_3_loss_25: 0.0161 - dense_3_loss_26: 0.0161 - dense_3_loss_27: 0.0181 - dense_3_loss_28: 0.0190 - dense_3_loss_29: 0.0233 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 17/100
60/60 [==============================] - 1s - loss: 5.4850 - dense_3_loss_1: 3.6943 - dense_3_loss_2: 1.0414 - dense_3_loss_3: 0.2315 - dense_3_loss_4: 0.0637 - dense_3_loss_5: 0.0351 - dense_3_loss_6: 0.0297 - dense_3_loss_7: 0.0226 - dense_3_loss_8: 0.0208 - dense_3_loss_9: 0.0180 - dense_3_loss_10: 0.0156 - dense_3_loss_11: 0.0166 - dense_3_loss_12: 0.0161 - dense_3_loss_13: 0.0140 - dense_3_loss_14: 0.0150 - dense_3_loss_15: 0.0159 - dense_3_loss_16: 0.0163 - dense_3_loss_17: 0.0151 - dense_3_loss_18: 0.0150 - dense_3_loss_19: 0.0162 - dense_3_loss_20: 0.0170 - dense_3_loss_21: 0.0176 - dense_3_loss_22: 0.0162 - dense_3_loss_23: 0.0150 - dense_3_loss_24: 0.0150 - dense_3_loss_25: 0.0159 - dense_3_loss_26: 0.0159 - dense_3_loss_27: 0.0178 - dense_3_loss_28: 0.0187 - dense_3_loss_29: 0.0230 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.0500 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 18/100
60/60 [==============================] - 1s - loss: 5.4657 - dense_3_loss_1: 3.6918 - dense_3_loss_2: 1.0345 - dense_3_loss_3: 0.2288 - dense_3_loss_4: 0.0629 - dense_3_loss_5: 0.0346 - dense_3_loss_6: 0.0293 - dense_3_loss_7: 0.0223 - dense_3_loss_8: 0.0205 - dense_3_loss_9: 0.0177 - dense_3_loss_10: 0.0153 - dense_3_loss_11: 0.0164 - dense_3_loss_12: 0.0159 - dense_3_loss_13: 0.0138 - dense_3_loss_14: 0.0148 - dense_3_loss_15: 0.0156 - dense_3_loss_16: 0.0161 - dense_3_loss_17: 0.0149 - dense_3_loss_18: 0.0148 - dense_3_loss_19: 0.0160 - dense_3_loss_20: 0.0168 - dense_3_loss_21: 0.0174 - dense_3_loss_22: 0.0160 - dense_3_loss_23: 0.0147 - dense_3_loss_24: 0.0148 - dense_3_loss_25: 0.0157 - dense_3_loss_26: 0.0156 - dense_3_loss_27: 0.0175 - dense_3_loss_28: 0.0184 - dense_3_loss_29: 0.0227 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 19/100
60/60 [==============================] - 1s - loss: 5.4476 - dense_3_loss_1: 3.6895 - dense_3_loss_2: 1.0283 - dense_3_loss_3: 0.2262 - dense_3_loss_4: 0.0622 - dense_3_loss_5: 0.0342 - dense_3_loss_6: 0.0289 - dense_3_loss_7: 0.0220 - dense_3_loss_8: 0.0202 - dense_3_loss_9: 0.0175 - dense_3_loss_10: 0.0151 - dense_3_loss_11: 0.0161 - dense_3_loss_12: 0.0157 - dense_3_loss_13: 0.0137 - dense_3_loss_14: 0.0146 - dense_3_loss_15: 0.0154 - dense_3_loss_16: 0.0159 - dense_3_loss_17: 0.0147 - dense_3_loss_18: 0.0146 - dense_3_loss_19: 0.0157 - dense_3_loss_20: 0.0165 - dense_3_loss_21: 0.0171 - dense_3_loss_22: 0.0157 - dense_3_loss_23: 0.0145 - dense_3_loss_24: 0.0145 - dense_3_loss_25: 0.0154 - dense_3_loss_26: 0.0154 - dense_3_loss_27: 0.0173 - dense_3_loss_28: 0.0182 - dense_3_loss_29: 0.0223 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 20/100
60/60 [==============================] - 1s - loss: 5.4297 - dense_3_loss_1: 3.6871 - dense_3_loss_2: 1.0216 - dense_3_loss_3: 0.2241 - dense_3_loss_4: 0.0615 - dense_3_loss_5: 0.0339 - dense_3_loss_6: 0.0285 - dense_3_loss_7: 0.0218 - dense_3_loss_8: 0.0200 - dense_3_loss_9: 0.0172 - dense_3_loss_10: 0.0150 - dense_3_loss_11: 0.0159 - dense_3_loss_12: 0.0155 - dense_3_loss_13: 0.0135 - dense_3_loss_14: 0.0144 - dense_3_loss_15: 0.0152 - dense_3_loss_16: 0.0156 - dense_3_loss_17: 0.0144 - dense_3_loss_18: 0.0144 - dense_3_loss_19: 0.0155 - dense_3_loss_20: 0.0163 - dense_3_loss_21: 0.0168 - dense_3_loss_22: 0.0155 - dense_3_loss_23: 0.0143 - dense_3_loss_24: 0.0144 - dense_3_loss_25: 0.0152 - dense_3_loss_26: 0.0152 - dense_3_loss_27: 0.0170 - dense_3_loss_28: 0.0180 - dense_3_loss_29: 0.0220 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9500 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 21/100
60/60 [==============================] - 1s - loss: 5.4117 - dense_3_loss_1: 3.6847 - dense_3_loss_2: 1.0150 - dense_3_loss_3: 0.2216 - dense_3_loss_4: 0.0608 - dense_3_loss_5: 0.0335 - dense_3_loss_6: 0.0281 - dense_3_loss_7: 0.0215 - dense_3_loss_8: 0.0197 - dense_3_loss_9: 0.0170 - dense_3_loss_10: 0.0148 - dense_3_loss_11: 0.0157 - dense_3_loss_12: 0.0152 - dense_3_loss_13: 0.0133 - dense_3_loss_14: 0.0142 - dense_3_loss_15: 0.0150 - dense_3_loss_16: 0.0154 - dense_3_loss_17: 0.0143 - dense_3_loss_18: 0.0142 - dense_3_loss_19: 0.0153 - dense_3_loss_20: 0.0161 - dense_3_loss_21: 0.0166 - dense_3_loss_22: 0.0153 - dense_3_loss_23: 0.0141 - dense_3_loss_24: 0.0142 - dense_3_loss_25: 0.0150 - dense_3_loss_26: 0.0149 - dense_3_loss_27: 0.0168 - dense_3_loss_28: 0.0177 - dense_3_loss_29: 0.0216 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 22/100
60/60 [==============================] - 1s - loss: 5.3934 - dense_3_loss_1: 3.6823 - dense_3_loss_2: 1.0081 - dense_3_loss_3: 0.2192 - dense_3_loss_4: 0.0600 - dense_3_loss_5: 0.0331 - dense_3_loss_6: 0.0277 - dense_3_loss_7: 0.0212 - dense_3_loss_8: 0.0194 - dense_3_loss_9: 0.0167 - dense_3_loss_10: 0.0146 - dense_3_loss_11: 0.0155 - dense_3_loss_12: 0.0150 - dense_3_loss_13: 0.0131 - dense_3_loss_14: 0.0140 - dense_3_loss_15: 0.0148 - dense_3_loss_16: 0.0152 - dense_3_loss_17: 0.0141 - dense_3_loss_18: 0.0140 - dense_3_loss_19: 0.0151 - dense_3_loss_20: 0.0158 - dense_3_loss_21: 0.0164 - dense_3_loss_22: 0.0151 - dense_3_loss_23: 0.0140 - dense_3_loss_24: 0.0140 - dense_3_loss_25: 0.0148 - dense_3_loss_26: 0.0147 - dense_3_loss_27: 0.0166 - dense_3_loss_28: 0.0175 - dense_3_loss_29: 0.0213 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 23/100
60/60 [==============================] - 1s - loss: 5.3765 - dense_3_loss_1: 3.6799 - dense_3_loss_2: 1.0021 - dense_3_loss_3: 0.2172 - dense_3_loss_4: 0.0592 - dense_3_loss_5: 0.0327 - dense_3_loss_6: 0.0274 - dense_3_loss_7: 0.0209 - dense_3_loss_8: 0.0192 - dense_3_loss_9: 0.0165 - dense_3_loss_10: 0.0144 - dense_3_loss_11: 0.0153 - dense_3_loss_12: 0.0148 - dense_3_loss_13: 0.0129 - dense_3_loss_14: 0.0138 - dense_3_loss_15: 0.0146 - dense_3_loss_16: 0.0150 - dense_3_loss_17: 0.0139 - dense_3_loss_18: 0.0138 - dense_3_loss_19: 0.0149 - dense_3_loss_20: 0.0156 - dense_3_loss_21: 0.0161 - dense_3_loss_22: 0.0149 - dense_3_loss_23: 0.0138 - dense_3_loss_24: 0.0138 - dense_3_loss_25: 0.0146 - dense_3_loss_26: 0.0145 - dense_3_loss_27: 0.0163 - dense_3_loss_28: 0.0172 - dense_3_loss_29: 0.0210 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.6833 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 24/100
60/60 [==============================] - 1s - loss: 5.3595 - dense_3_loss_1: 3.6774 - dense_3_loss_2: 0.9961 - dense_3_loss_3: 0.2150 - dense_3_loss_4: 0.0584 - dense_3_loss_5: 0.0323 - dense_3_loss_6: 0.0270 - dense_3_loss_7: 0.0206 - dense_3_loss_8: 0.0189 - dense_3_loss_9: 0.0163 - dense_3_loss_10: 0.0142 - dense_3_loss_11: 0.0151 - dense_3_loss_12: 0.0146 - dense_3_loss_13: 0.0127 - dense_3_loss_14: 0.0136 - dense_3_loss_15: 0.0144 - dense_3_loss_16: 0.0148 - dense_3_loss_17: 0.0137 - dense_3_loss_18: 0.0136 - dense_3_loss_19: 0.0147 - dense_3_loss_20: 0.0154 - dense_3_loss_21: 0.0159 - dense_3_loss_22: 0.0147 - dense_3_loss_23: 0.0136 - dense_3_loss_24: 0.0136 - dense_3_loss_25: 0.0144 - dense_3_loss_26: 0.0143 - dense_3_loss_27: 0.0161 - dense_3_loss_28: 0.0170 - dense_3_loss_29: 0.0207 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7000 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 25/100
60/60 [==============================] - 1s - loss: 5.3429 - dense_3_loss_1: 3.6753 - dense_3_loss_2: 0.9902 - dense_3_loss_3: 0.2128 - dense_3_loss_4: 0.0576 - dense_3_loss_5: 0.0319 - dense_3_loss_6: 0.0267 - dense_3_loss_7: 0.0204 - dense_3_loss_8: 0.0186 - dense_3_loss_9: 0.0161 - dense_3_loss_10: 0.0140 - dense_3_loss_11: 0.0149 - dense_3_loss_12: 0.0144 - dense_3_loss_13: 0.0126 - dense_3_loss_14: 0.0134 - dense_3_loss_15: 0.0142 - dense_3_loss_16: 0.0146 - dense_3_loss_17: 0.0135 - dense_3_loss_18: 0.0135 - dense_3_loss_19: 0.0145 - dense_3_loss_20: 0.0152 - dense_3_loss_21: 0.0157 - dense_3_loss_22: 0.0145 - dense_3_loss_23: 0.0134 - dense_3_loss_24: 0.0134 - dense_3_loss_25: 0.0142 - dense_3_loss_26: 0.0142 - dense_3_loss_27: 0.0159 - dense_3_loss_28: 0.0167 - dense_3_loss_29: 0.0204 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7000 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 26/100
60/60 [==============================] - 1s - loss: 5.3274 - dense_3_loss_1: 3.6727 - dense_3_loss_2: 0.9845 - dense_3_loss_3: 0.2112 - dense_3_loss_4: 0.0570 - dense_3_loss_5: 0.0316 - dense_3_loss_6: 0.0263 - dense_3_loss_7: 0.0201 - dense_3_loss_8: 0.0184 - dense_3_loss_9: 0.0159 - dense_3_loss_10: 0.0138 - dense_3_loss_11: 0.0147 - dense_3_loss_12: 0.0142 - dense_3_loss_13: 0.0124 - dense_3_loss_14: 0.0133 - dense_3_loss_15: 0.0140 - dense_3_loss_16: 0.0144 - dense_3_loss_17: 0.0133 - dense_3_loss_18: 0.0133 - dense_3_loss_19: 0.0143 - dense_3_loss_20: 0.0150 - dense_3_loss_21: 0.0155 - dense_3_loss_22: 0.0144 - dense_3_loss_23: 0.0132 - dense_3_loss_24: 0.0133 - dense_3_loss_25: 0.0141 - dense_3_loss_26: 0.0140 - dense_3_loss_27: 0.0157 - dense_3_loss_28: 0.0165 - dense_3_loss_29: 0.0201 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7000 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 27/100
60/60 [==============================] - 1s - loss: 5.3113 - dense_3_loss_1: 3.6705 - dense_3_loss_2: 0.9785 - dense_3_loss_3: 0.2092 - dense_3_loss_4: 0.0562 - dense_3_loss_5: 0.0312 - dense_3_loss_6: 0.0260 - dense_3_loss_7: 0.0199 - dense_3_loss_8: 0.0182 - dense_3_loss_9: 0.0157 - dense_3_loss_10: 0.0137 - dense_3_loss_11: 0.0145 - dense_3_loss_12: 0.0140 - dense_3_loss_13: 0.0122 - dense_3_loss_14: 0.0131 - dense_3_loss_15: 0.0139 - dense_3_loss_16: 0.0143 - dense_3_loss_17: 0.0132 - dense_3_loss_18: 0.0131 - dense_3_loss_19: 0.0141 - dense_3_loss_20: 0.0148 - dense_3_loss_21: 0.0153 - dense_3_loss_22: 0.0142 - dense_3_loss_23: 0.0131 - dense_3_loss_24: 0.0131 - dense_3_loss_25: 0.0139 - dense_3_loss_26: 0.0138 - dense_3_loss_27: 0.0155 - dense_3_loss_28: 0.0163 - dense_3_loss_29: 0.0199 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7000 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 28/100
60/60 [==============================] - 1s - loss: 5.2960 - dense_3_loss_1: 3.6681 - dense_3_loss_2: 0.9729 - dense_3_loss_3: 0.2074 - dense_3_loss_4: 0.0556 - dense_3_loss_5: 0.0309 - dense_3_loss_6: 0.0257 - dense_3_loss_7: 0.0196 - dense_3_loss_8: 0.0179 - dense_3_loss_9: 0.0155 - dense_3_loss_10: 0.0135 - dense_3_loss_11: 0.0144 - dense_3_loss_12: 0.0139 - dense_3_loss_13: 0.0121 - dense_3_loss_14: 0.0129 - dense_3_loss_15: 0.0137 - dense_3_loss_16: 0.0141 - dense_3_loss_17: 0.0130 - dense_3_loss_18: 0.0130 - dense_3_loss_19: 0.0140 - dense_3_loss_20: 0.0146 - dense_3_loss_21: 0.0151 - dense_3_loss_22: 0.0140 - dense_3_loss_23: 0.0129 - dense_3_loss_24: 0.0129 - dense_3_loss_25: 0.0137 - dense_3_loss_26: 0.0136 - dense_3_loss_27: 0.0153 - dense_3_loss_28: 0.0161 - dense_3_loss_29: 0.0196 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7000 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 29/100
60/60 [==============================] - 1s - loss: 5.2804 - dense_3_loss_1: 3.6659 - dense_3_loss_2: 0.9669 - dense_3_loss_3: 0.2057 - dense_3_loss_4: 0.0549 - dense_3_loss_5: 0.0305 - dense_3_loss_6: 0.0254 - dense_3_loss_7: 0.0194 - dense_3_loss_8: 0.0177 - dense_3_loss_9: 0.0153 - dense_3_loss_10: 0.0133 - dense_3_loss_11: 0.0142 - dense_3_loss_12: 0.0137 - dense_3_loss_13: 0.0119 - dense_3_loss_14: 0.0128 - dense_3_loss_15: 0.0135 - dense_3_loss_16: 0.0139 - dense_3_loss_17: 0.0128 - dense_3_loss_18: 0.0128 - dense_3_loss_19: 0.0138 - dense_3_loss_20: 0.0144 - dense_3_loss_21: 0.0149 - dense_3_loss_22: 0.0138 - dense_3_loss_23: 0.0127 - dense_3_loss_24: 0.0128 - dense_3_loss_25: 0.0135 - dense_3_loss_26: 0.0135 - dense_3_loss_27: 0.0150 - dense_3_loss_28: 0.0159 - dense_3_loss_29: 0.0193 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 30/100
60/60 [==============================] - 1s - loss: 5.2651 - dense_3_loss_1: 3.6637 - dense_3_loss_2: 0.9614 - dense_3_loss_3: 0.2036 - dense_3_loss_4: 0.0543 - dense_3_loss_5: 0.0301 - dense_3_loss_6: 0.0251 - dense_3_loss_7: 0.0191 - dense_3_loss_8: 0.0175 - dense_3_loss_9: 0.0151 - dense_3_loss_10: 0.0132 - dense_3_loss_11: 0.0140 - dense_3_loss_12: 0.0135 - dense_3_loss_13: 0.0118 - dense_3_loss_14: 0.0126 - dense_3_loss_15: 0.0133 - dense_3_loss_16: 0.0138 - dense_3_loss_17: 0.0127 - dense_3_loss_18: 0.0126 - dense_3_loss_19: 0.0136 - dense_3_loss_20: 0.0143 - dense_3_loss_21: 0.0147 - dense_3_loss_22: 0.0136 - dense_3_loss_23: 0.0126 - dense_3_loss_24: 0.0126 - dense_3_loss_25: 0.0134 - dense_3_loss_26: 0.0133 - dense_3_loss_27: 0.0148 - dense_3_loss_28: 0.0157 - dense_3_loss_29: 0.0190 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 31/100
60/60 [==============================] - 1s - loss: 5.2510 - dense_3_loss_1: 3.6613 - dense_3_loss_2: 0.9566 - dense_3_loss_3: 0.2019 - dense_3_loss_4: 0.0536 - dense_3_loss_5: 0.0298 - dense_3_loss_6: 0.0248 - dense_3_loss_7: 0.0189 - dense_3_loss_8: 0.0173 - dense_3_loss_9: 0.0150 - dense_3_loss_10: 0.0130 - dense_3_loss_11: 0.0138 - dense_3_loss_12: 0.0134 - dense_3_loss_13: 0.0116 - dense_3_loss_14: 0.0125 - dense_3_loss_15: 0.0132 - dense_3_loss_16: 0.0136 - dense_3_loss_17: 0.0125 - dense_3_loss_18: 0.0125 - dense_3_loss_19: 0.0134 - dense_3_loss_20: 0.0141 - dense_3_loss_21: 0.0145 - dense_3_loss_22: 0.0135 - dense_3_loss_23: 0.0124 - dense_3_loss_24: 0.0125 - dense_3_loss_25: 0.0132 - dense_3_loss_26: 0.0131 - dense_3_loss_27: 0.0147 - dense_3_loss_28: 0.0155 - dense_3_loss_29: 0.0188 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 32/100
60/60 [==============================] - 1s - loss: 5.2367 - dense_3_loss_1: 3.6591 - dense_3_loss_2: 0.9509 - dense_3_loss_3: 0.2007 - dense_3_loss_4: 0.0530 - dense_3_loss_5: 0.0295 - dense_3_loss_6: 0.0245 - dense_3_loss_7: 0.0187 - dense_3_loss_8: 0.0171 - dense_3_loss_9: 0.0148 - dense_3_loss_10: 0.0128 - dense_3_loss_11: 0.0136 - dense_3_loss_12: 0.0132 - dense_3_loss_13: 0.0115 - dense_3_loss_14: 0.0123 - dense_3_loss_15: 0.0130 - dense_3_loss_16: 0.0135 - dense_3_loss_17: 0.0124 - dense_3_loss_18: 0.0123 - dense_3_loss_19: 0.0133 - dense_3_loss_20: 0.0139 - dense_3_loss_21: 0.0143 - dense_3_loss_22: 0.0133 - dense_3_loss_23: 0.0123 - dense_3_loss_24: 0.0123 - dense_3_loss_25: 0.0130 - dense_3_loss_26: 0.0130 - dense_3_loss_27: 0.0145 - dense_3_loss_28: 0.0153 - dense_3_loss_29: 0.0186 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 33/100
60/60 [==============================] - 1s - loss: 5.2222 - dense_3_loss_1: 3.6568 - dense_3_loss_2: 0.9457 - dense_3_loss_3: 0.1988 - dense_3_loss_4: 0.0523 - dense_3_loss_5: 0.0292 - dense_3_loss_6: 0.0242 - dense_3_loss_7: 0.0185 - dense_3_loss_8: 0.0169 - dense_3_loss_9: 0.0146 - dense_3_loss_10: 0.0127 - dense_3_loss_11: 0.0135 - dense_3_loss_12: 0.0130 - dense_3_loss_13: 0.0114 - dense_3_loss_14: 0.0122 - dense_3_loss_15: 0.0129 - dense_3_loss_16: 0.0133 - dense_3_loss_17: 0.0122 - dense_3_loss_18: 0.0122 - dense_3_loss_19: 0.0131 - dense_3_loss_20: 0.0137 - dense_3_loss_21: 0.0142 - dense_3_loss_22: 0.0132 - dense_3_loss_23: 0.0121 - dense_3_loss_24: 0.0122 - dense_3_loss_25: 0.0129 - dense_3_loss_26: 0.0128 - dense_3_loss_27: 0.0143 - dense_3_loss_28: 0.0151 - dense_3_loss_29: 0.0183 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 34/100
60/60 [==============================] - 1s - loss: 5.2088 - dense_3_loss_1: 3.6546 - dense_3_loss_2: 0.9407 - dense_3_loss_3: 0.1973 - dense_3_loss_4: 0.0518 - dense_3_loss_5: 0.0289 - dense_3_loss_6: 0.0239 - dense_3_loss_7: 0.0183 - dense_3_loss_8: 0.0167 - dense_3_loss_9: 0.0145 - dense_3_loss_10: 0.0126 - dense_3_loss_11: 0.0133 - dense_3_loss_12: 0.0129 - dense_3_loss_13: 0.0112 - dense_3_loss_14: 0.0120 - dense_3_loss_15: 0.0127 - dense_3_loss_16: 0.0132 - dense_3_loss_17: 0.0120 - dense_3_loss_18: 0.0120 - dense_3_loss_19: 0.0130 - dense_3_loss_20: 0.0136 - dense_3_loss_21: 0.0140 - dense_3_loss_22: 0.0130 - dense_3_loss_23: 0.0120 - dense_3_loss_24: 0.0121 - dense_3_loss_25: 0.0127 - dense_3_loss_26: 0.0127 - dense_3_loss_27: 0.0141 - dense_3_loss_28: 0.0150 - dense_3_loss_29: 0.0181 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 35/100
60/60 [==============================] - 1s - loss: 5.1950 - dense_3_loss_1: 3.6524 - dense_3_loss_2: 0.9358 - dense_3_loss_3: 0.1955 - dense_3_loss_4: 0.0512 - dense_3_loss_5: 0.0286 - dense_3_loss_6: 0.0236 - dense_3_loss_7: 0.0181 - dense_3_loss_8: 0.0165 - dense_3_loss_9: 0.0143 - dense_3_loss_10: 0.0124 - dense_3_loss_11: 0.0132 - dense_3_loss_12: 0.0127 - dense_3_loss_13: 0.0111 - dense_3_loss_14: 0.0119 - dense_3_loss_15: 0.0126 - dense_3_loss_16: 0.0130 - dense_3_loss_17: 0.0119 - dense_3_loss_18: 0.0119 - dense_3_loss_19: 0.0128 - dense_3_loss_20: 0.0134 - dense_3_loss_21: 0.0139 - dense_3_loss_22: 0.0128 - dense_3_loss_23: 0.0118 - dense_3_loss_24: 0.0119 - dense_3_loss_25: 0.0126 - dense_3_loss_26: 0.0125 - dense_3_loss_27: 0.0140 - dense_3_loss_28: 0.0148 - dense_3_loss_29: 0.0179 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 36/100
60/60 [==============================] - 1s - loss: 5.1817 - dense_3_loss_1: 3.6502 - dense_3_loss_2: 0.9309 - dense_3_loss_3: 0.1939 - dense_3_loss_4: 0.0507 - dense_3_loss_5: 0.0283 - dense_3_loss_6: 0.0234 - dense_3_loss_7: 0.0179 - dense_3_loss_8: 0.0163 - dense_3_loss_9: 0.0142 - dense_3_loss_10: 0.0123 - dense_3_loss_11: 0.0130 - dense_3_loss_12: 0.0126 - dense_3_loss_13: 0.0110 - dense_3_loss_14: 0.0117 - dense_3_loss_15: 0.0124 - dense_3_loss_16: 0.0129 - dense_3_loss_17: 0.0118 - dense_3_loss_18: 0.0117 - dense_3_loss_19: 0.0126 - dense_3_loss_20: 0.0133 - dense_3_loss_21: 0.0137 - dense_3_loss_22: 0.0127 - dense_3_loss_23: 0.0117 - dense_3_loss_24: 0.0118 - dense_3_loss_25: 0.0124 - dense_3_loss_26: 0.0124 - dense_3_loss_27: 0.0138 - dense_3_loss_28: 0.0146 - dense_3_loss_29: 0.0177 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 37/100
60/60 [==============================] - 1s - loss: 5.1689 - dense_3_loss_1: 3.6480 - dense_3_loss_2: 0.9266 - dense_3_loss_3: 0.1922 - dense_3_loss_4: 0.0501 - dense_3_loss_5: 0.0280 - dense_3_loss_6: 0.0232 - dense_3_loss_7: 0.0177 - dense_3_loss_8: 0.0161 - dense_3_loss_9: 0.0140 - dense_3_loss_10: 0.0121 - dense_3_loss_11: 0.0129 - dense_3_loss_12: 0.0124 - dense_3_loss_13: 0.0109 - dense_3_loss_14: 0.0116 - dense_3_loss_15: 0.0123 - dense_3_loss_16: 0.0127 - dense_3_loss_17: 0.0116 - dense_3_loss_18: 0.0116 - dense_3_loss_19: 0.0125 - dense_3_loss_20: 0.0131 - dense_3_loss_21: 0.0136 - dense_3_loss_22: 0.0125 - dense_3_loss_23: 0.0116 - dense_3_loss_24: 0.0116 - dense_3_loss_25: 0.0123 - dense_3_loss_26: 0.0122 - dense_3_loss_27: 0.0137 - dense_3_loss_28: 0.0144 - dense_3_loss_29: 0.0175 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 38/100
60/60 [==============================] - 1s - loss: 5.1561 - dense_3_loss_1: 3.6459 - dense_3_loss_2: 0.9216 - dense_3_loss_3: 0.1910 - dense_3_loss_4: 0.0496 - dense_3_loss_5: 0.0277 - dense_3_loss_6: 0.0229 - dense_3_loss_7: 0.0175 - dense_3_loss_8: 0.0159 - dense_3_loss_9: 0.0138 - dense_3_loss_10: 0.0120 - dense_3_loss_11: 0.0127 - dense_3_loss_12: 0.0123 - dense_3_loss_13: 0.0107 - dense_3_loss_14: 0.0114 - dense_3_loss_15: 0.0122 - dense_3_loss_16: 0.0126 - dense_3_loss_17: 0.0115 - dense_3_loss_18: 0.0115 - dense_3_loss_19: 0.0124 - dense_3_loss_20: 0.0129 - dense_3_loss_21: 0.0134 - dense_3_loss_22: 0.0124 - dense_3_loss_23: 0.0114 - dense_3_loss_24: 0.0115 - dense_3_loss_25: 0.0122 - dense_3_loss_26: 0.0121 - dense_3_loss_27: 0.0135 - dense_3_loss_28: 0.0142 - dense_3_loss_29: 0.0172 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 39/100
60/60 [==============================] - 1s - loss: 5.1427 - dense_3_loss_1: 3.6438 - dense_3_loss_2: 0.9165 - dense_3_loss_3: 0.1894 - dense_3_loss_4: 0.0489 - dense_3_loss_5: 0.0274 - dense_3_loss_6: 0.0226 - dense_3_loss_7: 0.0173 - dense_3_loss_8: 0.0158 - dense_3_loss_9: 0.0137 - dense_3_loss_10: 0.0118 - dense_3_loss_11: 0.0126 - dense_3_loss_12: 0.0121 - dense_3_loss_13: 0.0106 - dense_3_loss_14: 0.0113 - dense_3_loss_15: 0.0120 - dense_3_loss_16: 0.0124 - dense_3_loss_17: 0.0114 - dense_3_loss_18: 0.0113 - dense_3_loss_19: 0.0122 - dense_3_loss_20: 0.0128 - dense_3_loss_21: 0.0132 - dense_3_loss_22: 0.0123 - dense_3_loss_23: 0.0113 - dense_3_loss_24: 0.0114 - dense_3_loss_25: 0.0120 - dense_3_loss_26: 0.0119 - dense_3_loss_27: 0.0134 - dense_3_loss_28: 0.0141 - dense_3_loss_29: 0.0170 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 40/100
60/60 [==============================] - 1s - loss: 5.1305 - dense_3_loss_1: 3.6416 - dense_3_loss_2: 0.9124 - dense_3_loss_3: 0.1878 - dense_3_loss_4: 0.0484 - dense_3_loss_5: 0.0271 - dense_3_loss_6: 0.0224 - dense_3_loss_7: 0.0171 - dense_3_loss_8: 0.0156 - dense_3_loss_9: 0.0136 - dense_3_loss_10: 0.0117 - dense_3_loss_11: 0.0125 - dense_3_loss_12: 0.0120 - dense_3_loss_13: 0.0105 - dense_3_loss_14: 0.0112 - dense_3_loss_15: 0.0119 - dense_3_loss_16: 0.0123 - dense_3_loss_17: 0.0112 - dense_3_loss_18: 0.0112 - dense_3_loss_19: 0.0121 - dense_3_loss_20: 0.0126 - dense_3_loss_21: 0.0131 - dense_3_loss_22: 0.0121 - dense_3_loss_23: 0.0112 - dense_3_loss_24: 0.0113 - dense_3_loss_25: 0.0119 - dense_3_loss_26: 0.0118 - dense_3_loss_27: 0.0132 - dense_3_loss_28: 0.0139 - dense_3_loss_29: 0.0168 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 41/100
60/60 [==============================] - 1s - loss: 5.1185 - dense_3_loss_1: 3.6395 - dense_3_loss_2: 0.9080 - dense_3_loss_3: 0.1865 - dense_3_loss_4: 0.0480 - dense_3_loss_5: 0.0269 - dense_3_loss_6: 0.0222 - dense_3_loss_7: 0.0169 - dense_3_loss_8: 0.0154 - dense_3_loss_9: 0.0134 - dense_3_loss_10: 0.0116 - dense_3_loss_11: 0.0123 - dense_3_loss_12: 0.0119 - dense_3_loss_13: 0.0104 - dense_3_loss_14: 0.0111 - dense_3_loss_15: 0.0117 - dense_3_loss_16: 0.0122 - dense_3_loss_17: 0.0111 - dense_3_loss_18: 0.0111 - dense_3_loss_19: 0.0120 - dense_3_loss_20: 0.0125 - dense_3_loss_21: 0.0129 - dense_3_loss_22: 0.0120 - dense_3_loss_23: 0.0111 - dense_3_loss_24: 0.0112 - dense_3_loss_25: 0.0118 - dense_3_loss_26: 0.0117 - dense_3_loss_27: 0.0131 - dense_3_loss_28: 0.0138 - dense_3_loss_29: 0.0166 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 42/100
60/60 [==============================] - 1s - loss: 5.1068 - dense_3_loss_1: 3.6374 - dense_3_loss_2: 0.9038 - dense_3_loss_3: 0.1851 - dense_3_loss_4: 0.0475 - dense_3_loss_5: 0.0266 - dense_3_loss_6: 0.0219 - dense_3_loss_7: 0.0167 - dense_3_loss_8: 0.0152 - dense_3_loss_9: 0.0133 - dense_3_loss_10: 0.0115 - dense_3_loss_11: 0.0122 - dense_3_loss_12: 0.0117 - dense_3_loss_13: 0.0103 - dense_3_loss_14: 0.0109 - dense_3_loss_15: 0.0116 - dense_3_loss_16: 0.0120 - dense_3_loss_17: 0.0110 - dense_3_loss_18: 0.0109 - dense_3_loss_19: 0.0118 - dense_3_loss_20: 0.0124 - dense_3_loss_21: 0.0128 - dense_3_loss_22: 0.0119 - dense_3_loss_23: 0.0110 - dense_3_loss_24: 0.0110 - dense_3_loss_25: 0.0116 - dense_3_loss_26: 0.0115 - dense_3_loss_27: 0.0129 - dense_3_loss_28: 0.0137 - dense_3_loss_29: 0.0164 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 43/100
60/60 [==============================] - 1s - loss: 5.0955 - dense_3_loss_1: 3.6355 - dense_3_loss_2: 0.8996 - dense_3_loss_3: 0.1839 - dense_3_loss_4: 0.0470 - dense_3_loss_5: 0.0264 - dense_3_loss_6: 0.0217 - dense_3_loss_7: 0.0165 - dense_3_loss_8: 0.0151 - dense_3_loss_9: 0.0131 - dense_3_loss_10: 0.0113 - dense_3_loss_11: 0.0121 - dense_3_loss_12: 0.0116 - dense_3_loss_13: 0.0102 - dense_3_loss_14: 0.0108 - dense_3_loss_15: 0.0115 - dense_3_loss_16: 0.0119 - dense_3_loss_17: 0.0109 - dense_3_loss_18: 0.0108 - dense_3_loss_19: 0.0117 - dense_3_loss_20: 0.0122 - dense_3_loss_21: 0.0127 - dense_3_loss_22: 0.0117 - dense_3_loss_23: 0.0108 - dense_3_loss_24: 0.0109 - dense_3_loss_25: 0.0115 - dense_3_loss_26: 0.0114 - dense_3_loss_27: 0.0128 - dense_3_loss_28: 0.0135 - dense_3_loss_29: 0.0162 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 44/100
60/60 [==============================] - 1s - loss: 5.0832 - dense_3_loss_1: 3.6334 - dense_3_loss_2: 0.8951 - dense_3_loss_3: 0.1822 - dense_3_loss_4: 0.0466 - dense_3_loss_5: 0.0261 - dense_3_loss_6: 0.0214 - dense_3_loss_7: 0.0164 - dense_3_loss_8: 0.0149 - dense_3_loss_9: 0.0130 - dense_3_loss_10: 0.0112 - dense_3_loss_11: 0.0119 - dense_3_loss_12: 0.0115 - dense_3_loss_13: 0.0100 - dense_3_loss_14: 0.0107 - dense_3_loss_15: 0.0114 - dense_3_loss_16: 0.0118 - dense_3_loss_17: 0.0108 - dense_3_loss_18: 0.0107 - dense_3_loss_19: 0.0116 - dense_3_loss_20: 0.0121 - dense_3_loss_21: 0.0125 - dense_3_loss_22: 0.0116 - dense_3_loss_23: 0.0107 - dense_3_loss_24: 0.0108 - dense_3_loss_25: 0.0114 - dense_3_loss_26: 0.0113 - dense_3_loss_27: 0.0126 - dense_3_loss_28: 0.0134 - dense_3_loss_29: 0.0161 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 45/100
60/60 [==============================] - 1s - loss: 5.0719 - dense_3_loss_1: 3.6313 - dense_3_loss_2: 0.8909 - dense_3_loss_3: 0.1811 - dense_3_loss_4: 0.0461 - dense_3_loss_5: 0.0259 - dense_3_loss_6: 0.0211 - dense_3_loss_7: 0.0162 - dense_3_loss_8: 0.0148 - dense_3_loss_9: 0.0129 - dense_3_loss_10: 0.0111 - dense_3_loss_11: 0.0118 - dense_3_loss_12: 0.0114 - dense_3_loss_13: 0.0099 - dense_3_loss_14: 0.0106 - dense_3_loss_15: 0.0112 - dense_3_loss_16: 0.0117 - dense_3_loss_17: 0.0106 - dense_3_loss_18: 0.0106 - dense_3_loss_19: 0.0114 - dense_3_loss_20: 0.0120 - dense_3_loss_21: 0.0124 - dense_3_loss_22: 0.0115 - dense_3_loss_23: 0.0106 - dense_3_loss_24: 0.0107 - dense_3_loss_25: 0.0113 - dense_3_loss_26: 0.0112 - dense_3_loss_27: 0.0125 - dense_3_loss_28: 0.0132 - dense_3_loss_29: 0.0159 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 46/100
60/60 [==============================] - 1s - loss: 5.0609 - dense_3_loss_1: 3.6292 - dense_3_loss_2: 0.8871 - dense_3_loss_3: 0.1798 - dense_3_loss_4: 0.0457 - dense_3_loss_5: 0.0257 - dense_3_loss_6: 0.0209 - dense_3_loss_7: 0.0160 - dense_3_loss_8: 0.0146 - dense_3_loss_9: 0.0127 - dense_3_loss_10: 0.0110 - dense_3_loss_11: 0.0117 - dense_3_loss_12: 0.0112 - dense_3_loss_13: 0.0098 - dense_3_loss_14: 0.0105 - dense_3_loss_15: 0.0111 - dense_3_loss_16: 0.0115 - dense_3_loss_17: 0.0105 - dense_3_loss_18: 0.0105 - dense_3_loss_19: 0.0113 - dense_3_loss_20: 0.0118 - dense_3_loss_21: 0.0123 - dense_3_loss_22: 0.0114 - dense_3_loss_23: 0.0105 - dense_3_loss_24: 0.0106 - dense_3_loss_25: 0.0111 - dense_3_loss_26: 0.0110 - dense_3_loss_27: 0.0124 - dense_3_loss_28: 0.0131 - dense_3_loss_29: 0.0157 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 47/100
60/60 [==============================] - 1s - loss: 5.0498 - dense_3_loss_1: 3.6272 - dense_3_loss_2: 0.8831 - dense_3_loss_3: 0.1784 - dense_3_loss_4: 0.0452 - dense_3_loss_5: 0.0254 - dense_3_loss_6: 0.0207 - dense_3_loss_7: 0.0159 - dense_3_loss_8: 0.0145 - dense_3_loss_9: 0.0126 - dense_3_loss_10: 0.0109 - dense_3_loss_11: 0.0116 - dense_3_loss_12: 0.0111 - dense_3_loss_13: 0.0097 - dense_3_loss_14: 0.0104 - dense_3_loss_15: 0.0110 - dense_3_loss_16: 0.0114 - dense_3_loss_17: 0.0104 - dense_3_loss_18: 0.0104 - dense_3_loss_19: 0.0112 - dense_3_loss_20: 0.0117 - dense_3_loss_21: 0.0121 - dense_3_loss_22: 0.0112 - dense_3_loss_23: 0.0104 - dense_3_loss_24: 0.0105 - dense_3_loss_25: 0.0110 - dense_3_loss_26: 0.0109 - dense_3_loss_27: 0.0122 - dense_3_loss_28: 0.0129 - dense_3_loss_29: 0.0155 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 48/100
60/60 [==============================] - 1s - loss: 5.0389 - dense_3_loss_1: 3.6252 - dense_3_loss_2: 0.8792 - dense_3_loss_3: 0.1772 - dense_3_loss_4: 0.0447 - dense_3_loss_5: 0.0251 - dense_3_loss_6: 0.0205 - dense_3_loss_7: 0.0157 - dense_3_loss_8: 0.0143 - dense_3_loss_9: 0.0125 - dense_3_loss_10: 0.0108 - dense_3_loss_11: 0.0115 - dense_3_loss_12: 0.0110 - dense_3_loss_13: 0.0096 - dense_3_loss_14: 0.0103 - dense_3_loss_15: 0.0109 - dense_3_loss_16: 0.0113 - dense_3_loss_17: 0.0103 - dense_3_loss_18: 0.0103 - dense_3_loss_19: 0.0111 - dense_3_loss_20: 0.0116 - dense_3_loss_21: 0.0120 - dense_3_loss_22: 0.0111 - dense_3_loss_23: 0.0103 - dense_3_loss_24: 0.0104 - dense_3_loss_25: 0.0109 - dense_3_loss_26: 0.0108 - dense_3_loss_27: 0.0121 - dense_3_loss_28: 0.0128 - dense_3_loss_29: 0.0154 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 49/100
60/60 [==============================] - 1s - loss: 5.0284 - dense_3_loss_1: 3.6232 - dense_3_loss_2: 0.8756 - dense_3_loss_3: 0.1760 - dense_3_loss_4: 0.0442 - dense_3_loss_5: 0.0249 - dense_3_loss_6: 0.0203 - dense_3_loss_7: 0.0156 - dense_3_loss_8: 0.0142 - dense_3_loss_9: 0.0124 - dense_3_loss_10: 0.0107 - dense_3_loss_11: 0.0113 - dense_3_loss_12: 0.0109 - dense_3_loss_13: 0.0095 - dense_3_loss_14: 0.0102 - dense_3_loss_15: 0.0108 - dense_3_loss_16: 0.0112 - dense_3_loss_17: 0.0102 - dense_3_loss_18: 0.0102 - dense_3_loss_19: 0.0110 - dense_3_loss_20: 0.0114 - dense_3_loss_21: 0.0119 - dense_3_loss_22: 0.0110 - dense_3_loss_23: 0.0102 - dense_3_loss_24: 0.0102 - dense_3_loss_25: 0.0108 - dense_3_loss_26: 0.0107 - dense_3_loss_27: 0.0120 - dense_3_loss_28: 0.0127 - dense_3_loss_29: 0.0152 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 50/100
60/60 [==============================] - 1s - loss: 5.0177 - dense_3_loss_1: 3.6213 - dense_3_loss_2: 0.8714 - dense_3_loss_3: 0.1750 - dense_3_loss_4: 0.0438 - dense_3_loss_5: 0.0247 - dense_3_loss_6: 0.0201 - dense_3_loss_7: 0.0154 - dense_3_loss_8: 0.0140 - dense_3_loss_9: 0.0122 - dense_3_loss_10: 0.0106 - dense_3_loss_11: 0.0112 - dense_3_loss_12: 0.0108 - dense_3_loss_13: 0.0094 - dense_3_loss_14: 0.0101 - dense_3_loss_15: 0.0107 - dense_3_loss_16: 0.0111 - dense_3_loss_17: 0.0101 - dense_3_loss_18: 0.0101 - dense_3_loss_19: 0.0108 - dense_3_loss_20: 0.0113 - dense_3_loss_21: 0.0117 - dense_3_loss_22: 0.0109 - dense_3_loss_23: 0.0101 - dense_3_loss_24: 0.0101 - dense_3_loss_25: 0.0107 - dense_3_loss_26: 0.0106 - dense_3_loss_27: 0.0119 - dense_3_loss_28: 0.0125 - dense_3_loss_29: 0.0150 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 51/100
60/60 [==============================] - 1s - loss: 5.0079 - dense_3_loss_1: 3.6193 - dense_3_loss_2: 0.8680 - dense_3_loss_3: 0.1738 - dense_3_loss_4: 0.0434 - dense_3_loss_5: 0.0244 - dense_3_loss_6: 0.0199 - dense_3_loss_7: 0.0153 - dense_3_loss_8: 0.0139 - dense_3_loss_9: 0.0121 - dense_3_loss_10: 0.0105 - dense_3_loss_11: 0.0111 - dense_3_loss_12: 0.0107 - dense_3_loss_13: 0.0093 - dense_3_loss_14: 0.0100 - dense_3_loss_15: 0.0106 - dense_3_loss_16: 0.0110 - dense_3_loss_17: 0.0100 - dense_3_loss_18: 0.0100 - dense_3_loss_19: 0.0107 - dense_3_loss_20: 0.0112 - dense_3_loss_21: 0.0116 - dense_3_loss_22: 0.0108 - dense_3_loss_23: 0.0100 - dense_3_loss_24: 0.0100 - dense_3_loss_25: 0.0106 - dense_3_loss_26: 0.0105 - dense_3_loss_27: 0.0117 - dense_3_loss_28: 0.0124 - dense_3_loss_29: 0.0149 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 52/100
60/60 [==============================] - 1s - loss: 4.9978 - dense_3_loss_1: 3.6174 - dense_3_loss_2: 0.8642 - dense_3_loss_3: 0.1727 - dense_3_loss_4: 0.0431 - dense_3_loss_5: 0.0242 - dense_3_loss_6: 0.0198 - dense_3_loss_7: 0.0152 - dense_3_loss_8: 0.0138 - dense_3_loss_9: 0.0120 - dense_3_loss_10: 0.0104 - dense_3_loss_11: 0.0110 - dense_3_loss_12: 0.0106 - dense_3_loss_13: 0.0092 - dense_3_loss_14: 0.0099 - dense_3_loss_15: 0.0105 - dense_3_loss_16: 0.0109 - dense_3_loss_17: 0.0099 - dense_3_loss_18: 0.0099 - dense_3_loss_19: 0.0106 - dense_3_loss_20: 0.0111 - dense_3_loss_21: 0.0115 - dense_3_loss_22: 0.0107 - dense_3_loss_23: 0.0099 - dense_3_loss_24: 0.0099 - dense_3_loss_25: 0.0105 - dense_3_loss_26: 0.0104 - dense_3_loss_27: 0.0116 - dense_3_loss_28: 0.0123 - dense_3_loss_29: 0.0147 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 53/100
60/60 [==============================] - 1s - loss: 4.9878 - dense_3_loss_1: 3.6155 - dense_3_loss_2: 0.8608 - dense_3_loss_3: 0.1715 - dense_3_loss_4: 0.0427 - dense_3_loss_5: 0.0240 - dense_3_loss_6: 0.0196 - dense_3_loss_7: 0.0150 - dense_3_loss_8: 0.0136 - dense_3_loss_9: 0.0119 - dense_3_loss_10: 0.0103 - dense_3_loss_11: 0.0109 - dense_3_loss_12: 0.0105 - dense_3_loss_13: 0.0092 - dense_3_loss_14: 0.0098 - dense_3_loss_15: 0.0104 - dense_3_loss_16: 0.0108 - dense_3_loss_17: 0.0098 - dense_3_loss_18: 0.0098 - dense_3_loss_19: 0.0105 - dense_3_loss_20: 0.0110 - dense_3_loss_21: 0.0114 - dense_3_loss_22: 0.0106 - dense_3_loss_23: 0.0098 - dense_3_loss_24: 0.0098 - dense_3_loss_25: 0.0104 - dense_3_loss_26: 0.0103 - dense_3_loss_27: 0.0115 - dense_3_loss_28: 0.0122 - dense_3_loss_29: 0.0146 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 54/100
60/60 [==============================] - 1s - loss: 4.9779 - dense_3_loss_1: 3.6137 - dense_3_loss_2: 0.8568 - dense_3_loss_3: 0.1706 - dense_3_loss_4: 0.0422 - dense_3_loss_5: 0.0238 - dense_3_loss_6: 0.0194 - dense_3_loss_7: 0.0149 - dense_3_loss_8: 0.0135 - dense_3_loss_9: 0.0118 - dense_3_loss_10: 0.0102 - dense_3_loss_11: 0.0108 - dense_3_loss_12: 0.0104 - dense_3_loss_13: 0.0091 - dense_3_loss_14: 0.0097 - dense_3_loss_15: 0.0103 - dense_3_loss_16: 0.0107 - dense_3_loss_17: 0.0097 - dense_3_loss_18: 0.0097 - dense_3_loss_19: 0.0104 - dense_3_loss_20: 0.0109 - dense_3_loss_21: 0.0113 - dense_3_loss_22: 0.0105 - dense_3_loss_23: 0.0097 - dense_3_loss_24: 0.0097 - dense_3_loss_25: 0.0103 - dense_3_loss_26: 0.0101 - dense_3_loss_27: 0.0113 - dense_3_loss_28: 0.0120 - dense_3_loss_29: 0.0144 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 55/100
60/60 [==============================] - 1s - loss: 4.9683 - dense_3_loss_1: 3.6116 - dense_3_loss_2: 0.8536 - dense_3_loss_3: 0.1695 - dense_3_loss_4: 0.0419 - dense_3_loss_5: 0.0236 - dense_3_loss_6: 0.0192 - dense_3_loss_7: 0.0147 - dense_3_loss_8: 0.0134 - dense_3_loss_9: 0.0117 - dense_3_loss_10: 0.0101 - dense_3_loss_11: 0.0107 - dense_3_loss_12: 0.0103 - dense_3_loss_13: 0.0090 - dense_3_loss_14: 0.0096 - dense_3_loss_15: 0.0102 - dense_3_loss_16: 0.0106 - dense_3_loss_17: 0.0096 - dense_3_loss_18: 0.0096 - dense_3_loss_19: 0.0103 - dense_3_loss_20: 0.0108 - dense_3_loss_21: 0.0112 - dense_3_loss_22: 0.0104 - dense_3_loss_23: 0.0096 - dense_3_loss_24: 0.0096 - dense_3_loss_25: 0.0102 - dense_3_loss_26: 0.0100 - dense_3_loss_27: 0.0112 - dense_3_loss_28: 0.0119 - dense_3_loss_29: 0.0143 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 56/100
60/60 [==============================] - 1s - loss: 4.9589 - dense_3_loss_1: 3.6098 - dense_3_loss_2: 0.8502 - dense_3_loss_3: 0.1686 - dense_3_loss_4: 0.0415 - dense_3_loss_5: 0.0234 - dense_3_loss_6: 0.0190 - dense_3_loss_7: 0.0146 - dense_3_loss_8: 0.0132 - dense_3_loss_9: 0.0116 - dense_3_loss_10: 0.0100 - dense_3_loss_11: 0.0106 - dense_3_loss_12: 0.0102 - dense_3_loss_13: 0.0089 - dense_3_loss_14: 0.0095 - dense_3_loss_15: 0.0101 - dense_3_loss_16: 0.0105 - dense_3_loss_17: 0.0095 - dense_3_loss_18: 0.0095 - dense_3_loss_19: 0.0102 - dense_3_loss_20: 0.0106 - dense_3_loss_21: 0.0111 - dense_3_loss_22: 0.0103 - dense_3_loss_23: 0.0095 - dense_3_loss_24: 0.0095 - dense_3_loss_25: 0.0101 - dense_3_loss_26: 0.0099 - dense_3_loss_27: 0.0111 - dense_3_loss_28: 0.0118 - dense_3_loss_29: 0.0141 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 57/100
60/60 [==============================] - 1s - loss: 4.9496 - dense_3_loss_1: 3.6079 - dense_3_loss_2: 0.8469 - dense_3_loss_3: 0.1677 - dense_3_loss_4: 0.0411 - dense_3_loss_5: 0.0232 - dense_3_loss_6: 0.0188 - dense_3_loss_7: 0.0145 - dense_3_loss_8: 0.0131 - dense_3_loss_9: 0.0115 - dense_3_loss_10: 0.0099 - dense_3_loss_11: 0.0105 - dense_3_loss_12: 0.0101 - dense_3_loss_13: 0.0088 - dense_3_loss_14: 0.0094 - dense_3_loss_15: 0.0100 - dense_3_loss_16: 0.0104 - dense_3_loss_17: 0.0094 - dense_3_loss_18: 0.0094 - dense_3_loss_19: 0.0101 - dense_3_loss_20: 0.0105 - dense_3_loss_21: 0.0109 - dense_3_loss_22: 0.0102 - dense_3_loss_23: 0.0094 - dense_3_loss_24: 0.0095 - dense_3_loss_25: 0.0100 - dense_3_loss_26: 0.0098 - dense_3_loss_27: 0.0110 - dense_3_loss_28: 0.0117 - dense_3_loss_29: 0.0140 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 58/100
60/60 [==============================] - 1s - loss: 4.9404 - dense_3_loss_1: 3.6061 - dense_3_loss_2: 0.8435 - dense_3_loss_3: 0.1668 - dense_3_loss_4: 0.0407 - dense_3_loss_5: 0.0230 - dense_3_loss_6: 0.0186 - dense_3_loss_7: 0.0144 - dense_3_loss_8: 0.0130 - dense_3_loss_9: 0.0114 - dense_3_loss_10: 0.0098 - dense_3_loss_11: 0.0104 - dense_3_loss_12: 0.0100 - dense_3_loss_13: 0.0087 - dense_3_loss_14: 0.0093 - dense_3_loss_15: 0.0099 - dense_3_loss_16: 0.0103 - dense_3_loss_17: 0.0093 - dense_3_loss_18: 0.0093 - dense_3_loss_19: 0.0100 - dense_3_loss_20: 0.0104 - dense_3_loss_21: 0.0108 - dense_3_loss_22: 0.0101 - dense_3_loss_23: 0.0093 - dense_3_loss_24: 0.0094 - dense_3_loss_25: 0.0099 - dense_3_loss_26: 0.0097 - dense_3_loss_27: 0.0109 - dense_3_loss_28: 0.0116 - dense_3_loss_29: 0.0139 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 59/100
60/60 [==============================] - 1s - loss: 4.9314 - dense_3_loss_1: 3.6042 - dense_3_loss_2: 0.8402 - dense_3_loss_3: 0.1660 - dense_3_loss_4: 0.0403 - dense_3_loss_5: 0.0228 - dense_3_loss_6: 0.0184 - dense_3_loss_7: 0.0142 - dense_3_loss_8: 0.0129 - dense_3_loss_9: 0.0113 - dense_3_loss_10: 0.0097 - dense_3_loss_11: 0.0103 - dense_3_loss_12: 0.0099 - dense_3_loss_13: 0.0086 - dense_3_loss_14: 0.0092 - dense_3_loss_15: 0.0098 - dense_3_loss_16: 0.0102 - dense_3_loss_17: 0.0092 - dense_3_loss_18: 0.0092 - dense_3_loss_19: 0.0099 - dense_3_loss_20: 0.0103 - dense_3_loss_21: 0.0107 - dense_3_loss_22: 0.0100 - dense_3_loss_23: 0.0092 - dense_3_loss_24: 0.0093 - dense_3_loss_25: 0.0098 - dense_3_loss_26: 0.0096 - dense_3_loss_27: 0.0108 - dense_3_loss_28: 0.0115 - dense_3_loss_29: 0.0137 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 60/100
60/60 [==============================] - 1s - loss: 4.9224 - dense_3_loss_1: 3.6023 - dense_3_loss_2: 0.8370 - dense_3_loss_3: 0.1651 - dense_3_loss_4: 0.0399 - dense_3_loss_5: 0.0226 - dense_3_loss_6: 0.0183 - dense_3_loss_7: 0.0141 - dense_3_loss_8: 0.0127 - dense_3_loss_9: 0.0112 - dense_3_loss_10: 0.0096 - dense_3_loss_11: 0.0102 - dense_3_loss_12: 0.0098 - dense_3_loss_13: 0.0086 - dense_3_loss_14: 0.0092 - dense_3_loss_15: 0.0097 - dense_3_loss_16: 0.0101 - dense_3_loss_17: 0.0091 - dense_3_loss_18: 0.0092 - dense_3_loss_19: 0.0098 - dense_3_loss_20: 0.0102 - dense_3_loss_21: 0.0106 - dense_3_loss_22: 0.0099 - dense_3_loss_23: 0.0091 - dense_3_loss_24: 0.0092 - dense_3_loss_25: 0.0097 - dense_3_loss_26: 0.0095 - dense_3_loss_27: 0.0107 - dense_3_loss_28: 0.0114 - dense_3_loss_29: 0.0136 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 61/100
60/60 [==============================] - 1s - loss: 4.9135 - dense_3_loss_1: 3.6007 - dense_3_loss_2: 0.8337 - dense_3_loss_3: 0.1640 - dense_3_loss_4: 0.0396 - dense_3_loss_5: 0.0224 - dense_3_loss_6: 0.0181 - dense_3_loss_7: 0.0140 - dense_3_loss_8: 0.0126 - dense_3_loss_9: 0.0111 - dense_3_loss_10: 0.0096 - dense_3_loss_11: 0.0102 - dense_3_loss_12: 0.0097 - dense_3_loss_13: 0.0085 - dense_3_loss_14: 0.0091 - dense_3_loss_15: 0.0096 - dense_3_loss_16: 0.0100 - dense_3_loss_17: 0.0090 - dense_3_loss_18: 0.0091 - dense_3_loss_19: 0.0097 - dense_3_loss_20: 0.0101 - dense_3_loss_21: 0.0105 - dense_3_loss_22: 0.0098 - dense_3_loss_23: 0.0090 - dense_3_loss_24: 0.0091 - dense_3_loss_25: 0.0096 - dense_3_loss_26: 0.0094 - dense_3_loss_27: 0.0106 - dense_3_loss_28: 0.0113 - dense_3_loss_29: 0.0134 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 62/100
60/60 [==============================] - 1s - loss: 4.9050 - dense_3_loss_1: 3.5989 - dense_3_loss_2: 0.8308 - dense_3_loss_3: 0.1631 - dense_3_loss_4: 0.0392 - dense_3_loss_5: 0.0222 - dense_3_loss_6: 0.0179 - dense_3_loss_7: 0.0139 - dense_3_loss_8: 0.0125 - dense_3_loss_9: 0.0110 - dense_3_loss_10: 0.0095 - dense_3_loss_11: 0.0101 - dense_3_loss_12: 0.0096 - dense_3_loss_13: 0.0084 - dense_3_loss_14: 0.0090 - dense_3_loss_15: 0.0095 - dense_3_loss_16: 0.0099 - dense_3_loss_17: 0.0089 - dense_3_loss_18: 0.0090 - dense_3_loss_19: 0.0096 - dense_3_loss_20: 0.0100 - dense_3_loss_21: 0.0104 - dense_3_loss_22: 0.0097 - dense_3_loss_23: 0.0089 - dense_3_loss_24: 0.0090 - dense_3_loss_25: 0.0095 - dense_3_loss_26: 0.0093 - dense_3_loss_27: 0.0105 - dense_3_loss_28: 0.0112 - dense_3_loss_29: 0.0133 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 63/100
60/60 [==============================] - 1s - loss: 4.8970 - dense_3_loss_1: 3.5969 - dense_3_loss_2: 0.8281 - dense_3_loss_3: 0.1624 - dense_3_loss_4: 0.0389 - dense_3_loss_5: 0.0220 - dense_3_loss_6: 0.0178 - dense_3_loss_7: 0.0138 - dense_3_loss_8: 0.0124 - dense_3_loss_9: 0.0109 - dense_3_loss_10: 0.0094 - dense_3_loss_11: 0.0100 - dense_3_loss_12: 0.0095 - dense_3_loss_13: 0.0083 - dense_3_loss_14: 0.0089 - dense_3_loss_15: 0.0094 - dense_3_loss_16: 0.0098 - dense_3_loss_17: 0.0089 - dense_3_loss_18: 0.0089 - dense_3_loss_19: 0.0096 - dense_3_loss_20: 0.0099 - dense_3_loss_21: 0.0104 - dense_3_loss_22: 0.0096 - dense_3_loss_23: 0.0088 - dense_3_loss_24: 0.0089 - dense_3_loss_25: 0.0094 - dense_3_loss_26: 0.0092 - dense_3_loss_27: 0.0104 - dense_3_loss_28: 0.0110 - dense_3_loss_29: 0.0132 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 64/100
60/60 [==============================] - 1s - loss: 4.8886 - dense_3_loss_1: 3.5952 - dense_3_loss_2: 0.8249 - dense_3_loss_3: 0.1618 - dense_3_loss_4: 0.0385 - dense_3_loss_5: 0.0219 - dense_3_loss_6: 0.0176 - dense_3_loss_7: 0.0136 - dense_3_loss_8: 0.0123 - dense_3_loss_9: 0.0108 - dense_3_loss_10: 0.0093 - dense_3_loss_11: 0.0099 - dense_3_loss_12: 0.0094 - dense_3_loss_13: 0.0083 - dense_3_loss_14: 0.0088 - dense_3_loss_15: 0.0094 - dense_3_loss_16: 0.0097 - dense_3_loss_17: 0.0088 - dense_3_loss_18: 0.0088 - dense_3_loss_19: 0.0095 - dense_3_loss_20: 0.0098 - dense_3_loss_21: 0.0103 - dense_3_loss_22: 0.0095 - dense_3_loss_23: 0.0088 - dense_3_loss_24: 0.0088 - dense_3_loss_25: 0.0094 - dense_3_loss_26: 0.0091 - dense_3_loss_27: 0.0103 - dense_3_loss_28: 0.0109 - dense_3_loss_29: 0.0131 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 65/100
60/60 [==============================] - 1s - loss: 4.8803 - dense_3_loss_1: 3.5934 - dense_3_loss_2: 0.8220 - dense_3_loss_3: 0.1610 - dense_3_loss_4: 0.0382 - dense_3_loss_5: 0.0217 - dense_3_loss_6: 0.0175 - dense_3_loss_7: 0.0135 - dense_3_loss_8: 0.0122 - dense_3_loss_9: 0.0107 - dense_3_loss_10: 0.0092 - dense_3_loss_11: 0.0098 - dense_3_loss_12: 0.0093 - dense_3_loss_13: 0.0082 - dense_3_loss_14: 0.0087 - dense_3_loss_15: 0.0093 - dense_3_loss_16: 0.0097 - dense_3_loss_17: 0.0087 - dense_3_loss_18: 0.0087 - dense_3_loss_19: 0.0094 - dense_3_loss_20: 0.0098 - dense_3_loss_21: 0.0102 - dense_3_loss_22: 0.0094 - dense_3_loss_23: 0.0087 - dense_3_loss_24: 0.0088 - dense_3_loss_25: 0.0093 - dense_3_loss_26: 0.0091 - dense_3_loss_27: 0.0102 - dense_3_loss_28: 0.0108 - dense_3_loss_29: 0.0129 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 66/100
60/60 [==============================] - 1s - loss: 4.8727 - dense_3_loss_1: 3.5916 - dense_3_loss_2: 0.8194 - dense_3_loss_3: 0.1604 - dense_3_loss_4: 0.0378 - dense_3_loss_5: 0.0215 - dense_3_loss_6: 0.0173 - dense_3_loss_7: 0.0134 - dense_3_loss_8: 0.0121 - dense_3_loss_9: 0.0107 - dense_3_loss_10: 0.0092 - dense_3_loss_11: 0.0097 - dense_3_loss_12: 0.0093 - dense_3_loss_13: 0.0081 - dense_3_loss_14: 0.0087 - dense_3_loss_15: 0.0092 - dense_3_loss_16: 0.0096 - dense_3_loss_17: 0.0086 - dense_3_loss_18: 0.0087 - dense_3_loss_19: 0.0093 - dense_3_loss_20: 0.0097 - dense_3_loss_21: 0.0101 - dense_3_loss_22: 0.0094 - dense_3_loss_23: 0.0086 - dense_3_loss_24: 0.0087 - dense_3_loss_25: 0.0092 - dense_3_loss_26: 0.0090 - dense_3_loss_27: 0.0101 - dense_3_loss_28: 0.0107 - dense_3_loss_29: 0.0128 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 67/100
60/60 [==============================] - 1s - loss: 4.8642 - dense_3_loss_1: 3.5899 - dense_3_loss_2: 0.8163 - dense_3_loss_3: 0.1593 - dense_3_loss_4: 0.0375 - dense_3_loss_5: 0.0213 - dense_3_loss_6: 0.0172 - dense_3_loss_7: 0.0132 - dense_3_loss_8: 0.0120 - dense_3_loss_9: 0.0106 - dense_3_loss_10: 0.0091 - dense_3_loss_11: 0.0096 - dense_3_loss_12: 0.0092 - dense_3_loss_13: 0.0080 - dense_3_loss_14: 0.0086 - dense_3_loss_15: 0.0091 - dense_3_loss_16: 0.0095 - dense_3_loss_17: 0.0085 - dense_3_loss_18: 0.0086 - dense_3_loss_19: 0.0092 - dense_3_loss_20: 0.0096 - dense_3_loss_21: 0.0100 - dense_3_loss_22: 0.0093 - dense_3_loss_23: 0.0085 - dense_3_loss_24: 0.0086 - dense_3_loss_25: 0.0091 - dense_3_loss_26: 0.0089 - dense_3_loss_27: 0.0100 - dense_3_loss_28: 0.0106 - dense_3_loss_29: 0.0127 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 68/100
60/60 [==============================] - 1s - loss: 4.8563 - dense_3_loss_1: 3.5882 - dense_3_loss_2: 0.8136 - dense_3_loss_3: 0.1585 - dense_3_loss_4: 0.0372 - dense_3_loss_5: 0.0211 - dense_3_loss_6: 0.0171 - dense_3_loss_7: 0.0131 - dense_3_loss_8: 0.0119 - dense_3_loss_9: 0.0105 - dense_3_loss_10: 0.0090 - dense_3_loss_11: 0.0096 - dense_3_loss_12: 0.0091 - dense_3_loss_13: 0.0080 - dense_3_loss_14: 0.0085 - dense_3_loss_15: 0.0090 - dense_3_loss_16: 0.0094 - dense_3_loss_17: 0.0085 - dense_3_loss_18: 0.0085 - dense_3_loss_19: 0.0091 - dense_3_loss_20: 0.0095 - dense_3_loss_21: 0.0099 - dense_3_loss_22: 0.0092 - dense_3_loss_23: 0.0084 - dense_3_loss_24: 0.0085 - dense_3_loss_25: 0.0090 - dense_3_loss_26: 0.0088 - dense_3_loss_27: 0.0099 - dense_3_loss_28: 0.0106 - dense_3_loss_29: 0.0126 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 69/100
60/60 [==============================] - 1s - loss: 4.8487 - dense_3_loss_1: 3.5864 - dense_3_loss_2: 0.8108 - dense_3_loss_3: 0.1579 - dense_3_loss_4: 0.0369 - dense_3_loss_5: 0.0210 - dense_3_loss_6: 0.0169 - dense_3_loss_7: 0.0130 - dense_3_loss_8: 0.0118 - dense_3_loss_9: 0.0104 - dense_3_loss_10: 0.0089 - dense_3_loss_11: 0.0095 - dense_3_loss_12: 0.0090 - dense_3_loss_13: 0.0079 - dense_3_loss_14: 0.0084 - dense_3_loss_15: 0.0090 - dense_3_loss_16: 0.0093 - dense_3_loss_17: 0.0084 - dense_3_loss_18: 0.0084 - dense_3_loss_19: 0.0091 - dense_3_loss_20: 0.0094 - dense_3_loss_21: 0.0098 - dense_3_loss_22: 0.0091 - dense_3_loss_23: 0.0084 - dense_3_loss_24: 0.0085 - dense_3_loss_25: 0.0090 - dense_3_loss_26: 0.0087 - dense_3_loss_27: 0.0098 - dense_3_loss_28: 0.0105 - dense_3_loss_29: 0.0125 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 70/100
60/60 [==============================] - 1s - loss: 4.8409 - dense_3_loss_1: 3.5848 - dense_3_loss_2: 0.8082 - dense_3_loss_3: 0.1568 - dense_3_loss_4: 0.0367 - dense_3_loss_5: 0.0208 - dense_3_loss_6: 0.0168 - dense_3_loss_7: 0.0129 - dense_3_loss_8: 0.0117 - dense_3_loss_9: 0.0103 - dense_3_loss_10: 0.0089 - dense_3_loss_11: 0.0094 - dense_3_loss_12: 0.0089 - dense_3_loss_13: 0.0078 - dense_3_loss_14: 0.0084 - dense_3_loss_15: 0.0089 - dense_3_loss_16: 0.0093 - dense_3_loss_17: 0.0083 - dense_3_loss_18: 0.0084 - dense_3_loss_19: 0.0090 - dense_3_loss_20: 0.0093 - dense_3_loss_21: 0.0097 - dense_3_loss_22: 0.0090 - dense_3_loss_23: 0.0083 - dense_3_loss_24: 0.0084 - dense_3_loss_25: 0.0089 - dense_3_loss_26: 0.0087 - dense_3_loss_27: 0.0097 - dense_3_loss_28: 0.0104 - dense_3_loss_29: 0.0124 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 71/100
60/60 [==============================] - 1s - loss: 4.8337 - dense_3_loss_1: 3.5830 - dense_3_loss_2: 0.8058 - dense_3_loss_3: 0.1562 - dense_3_loss_4: 0.0364 - dense_3_loss_5: 0.0207 - dense_3_loss_6: 0.0166 - dense_3_loss_7: 0.0128 - dense_3_loss_8: 0.0116 - dense_3_loss_9: 0.0102 - dense_3_loss_10: 0.0088 - dense_3_loss_11: 0.0093 - dense_3_loss_12: 0.0089 - dense_3_loss_13: 0.0078 - dense_3_loss_14: 0.0083 - dense_3_loss_15: 0.0088 - dense_3_loss_16: 0.0092 - dense_3_loss_17: 0.0083 - dense_3_loss_18: 0.0083 - dense_3_loss_19: 0.0089 - dense_3_loss_20: 0.0092 - dense_3_loss_21: 0.0096 - dense_3_loss_22: 0.0089 - dense_3_loss_23: 0.0082 - dense_3_loss_24: 0.0083 - dense_3_loss_25: 0.0088 - dense_3_loss_26: 0.0086 - dense_3_loss_27: 0.0097 - dense_3_loss_28: 0.0103 - dense_3_loss_29: 0.0122 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 72/100
60/60 [==============================] - 1s - loss: 4.8261 - dense_3_loss_1: 3.5813 - dense_3_loss_2: 0.8031 - dense_3_loss_3: 0.1553 - dense_3_loss_4: 0.0361 - dense_3_loss_5: 0.0205 - dense_3_loss_6: 0.0165 - dense_3_loss_7: 0.0127 - dense_3_loss_8: 0.0115 - dense_3_loss_9: 0.0102 - dense_3_loss_10: 0.0087 - dense_3_loss_11: 0.0092 - dense_3_loss_12: 0.0088 - dense_3_loss_13: 0.0077 - dense_3_loss_14: 0.0082 - dense_3_loss_15: 0.0087 - dense_3_loss_16: 0.0091 - dense_3_loss_17: 0.0082 - dense_3_loss_18: 0.0082 - dense_3_loss_19: 0.0088 - dense_3_loss_20: 0.0092 - dense_3_loss_21: 0.0095 - dense_3_loss_22: 0.0089 - dense_3_loss_23: 0.0082 - dense_3_loss_24: 0.0083 - dense_3_loss_25: 0.0087 - dense_3_loss_26: 0.0085 - dense_3_loss_27: 0.0096 - dense_3_loss_28: 0.0102 - dense_3_loss_29: 0.0121 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 73/100
60/60 [==============================] - 1s - loss: 4.8186 - dense_3_loss_1: 3.5797 - dense_3_loss_2: 0.8005 - dense_3_loss_3: 0.1546 - dense_3_loss_4: 0.0358 - dense_3_loss_5: 0.0203 - dense_3_loss_6: 0.0163 - dense_3_loss_7: 0.0126 - dense_3_loss_8: 0.0114 - dense_3_loss_9: 0.0101 - dense_3_loss_10: 0.0086 - dense_3_loss_11: 0.0092 - dense_3_loss_12: 0.0087 - dense_3_loss_13: 0.0076 - dense_3_loss_14: 0.0082 - dense_3_loss_15: 0.0086 - dense_3_loss_16: 0.0090 - dense_3_loss_17: 0.0081 - dense_3_loss_18: 0.0081 - dense_3_loss_19: 0.0088 - dense_3_loss_20: 0.0091 - dense_3_loss_21: 0.0094 - dense_3_loss_22: 0.0088 - dense_3_loss_23: 0.0081 - dense_3_loss_24: 0.0082 - dense_3_loss_25: 0.0086 - dense_3_loss_26: 0.0084 - dense_3_loss_27: 0.0095 - dense_3_loss_28: 0.0101 - dense_3_loss_29: 0.0120 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 74/100
60/60 [==============================] - 1s - loss: 4.8116 - dense_3_loss_1: 3.5779 - dense_3_loss_2: 0.7982 - dense_3_loss_3: 0.1539 - dense_3_loss_4: 0.0355 - dense_3_loss_5: 0.0202 - dense_3_loss_6: 0.0162 - dense_3_loss_7: 0.0125 - dense_3_loss_8: 0.0113 - dense_3_loss_9: 0.0100 - dense_3_loss_10: 0.0086 - dense_3_loss_11: 0.0091 - dense_3_loss_12: 0.0086 - dense_3_loss_13: 0.0076 - dense_3_loss_14: 0.0081 - dense_3_loss_15: 0.0086 - dense_3_loss_16: 0.0090 - dense_3_loss_17: 0.0081 - dense_3_loss_18: 0.0081 - dense_3_loss_19: 0.0087 - dense_3_loss_20: 0.0090 - dense_3_loss_21: 0.0094 - dense_3_loss_22: 0.0087 - dense_3_loss_23: 0.0080 - dense_3_loss_24: 0.0081 - dense_3_loss_25: 0.0086 - dense_3_loss_26: 0.0084 - dense_3_loss_27: 0.0094 - dense_3_loss_28: 0.0101 - dense_3_loss_29: 0.0119 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 75/100
60/60 [==============================] - 1s - loss: 4.8048 - dense_3_loss_1: 3.5763 - dense_3_loss_2: 0.7959 - dense_3_loss_3: 0.1533 - dense_3_loss_4: 0.0352 - dense_3_loss_5: 0.0200 - dense_3_loss_6: 0.0161 - dense_3_loss_7: 0.0124 - dense_3_loss_8: 0.0112 - dense_3_loss_9: 0.0099 - dense_3_loss_10: 0.0085 - dense_3_loss_11: 0.0090 - dense_3_loss_12: 0.0086 - dense_3_loss_13: 0.0075 - dense_3_loss_14: 0.0080 - dense_3_loss_15: 0.0085 - dense_3_loss_16: 0.0089 - dense_3_loss_17: 0.0080 - dense_3_loss_18: 0.0080 - dense_3_loss_19: 0.0086 - dense_3_loss_20: 0.0089 - dense_3_loss_21: 0.0093 - dense_3_loss_22: 0.0086 - dense_3_loss_23: 0.0080 - dense_3_loss_24: 0.0081 - dense_3_loss_25: 0.0085 - dense_3_loss_26: 0.0083 - dense_3_loss_27: 0.0094 - dense_3_loss_28: 0.0100 - dense_3_loss_29: 0.0118 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 76/100
60/60 [==============================] - 1s - loss: 4.7977 - dense_3_loss_1: 3.5747 - dense_3_loss_2: 0.7932 - dense_3_loss_3: 0.1527 - dense_3_loss_4: 0.0350 - dense_3_loss_5: 0.0199 - dense_3_loss_6: 0.0159 - dense_3_loss_7: 0.0123 - dense_3_loss_8: 0.0111 - dense_3_loss_9: 0.0099 - dense_3_loss_10: 0.0084 - dense_3_loss_11: 0.0089 - dense_3_loss_12: 0.0085 - dense_3_loss_13: 0.0075 - dense_3_loss_14: 0.0079 - dense_3_loss_15: 0.0084 - dense_3_loss_16: 0.0088 - dense_3_loss_17: 0.0079 - dense_3_loss_18: 0.0080 - dense_3_loss_19: 0.0085 - dense_3_loss_20: 0.0089 - dense_3_loss_21: 0.0092 - dense_3_loss_22: 0.0086 - dense_3_loss_23: 0.0079 - dense_3_loss_24: 0.0080 - dense_3_loss_25: 0.0084 - dense_3_loss_26: 0.0082 - dense_3_loss_27: 0.0093 - dense_3_loss_28: 0.0099 - dense_3_loss_29: 0.0117 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 77/100
60/60 [==============================] - 1s - loss: 4.7906 - dense_3_loss_1: 3.5731 - dense_3_loss_2: 0.7908 - dense_3_loss_3: 0.1518 - dense_3_loss_4: 0.0347 - dense_3_loss_5: 0.0197 - dense_3_loss_6: 0.0158 - dense_3_loss_7: 0.0122 - dense_3_loss_8: 0.0111 - dense_3_loss_9: 0.0098 - dense_3_loss_10: 0.0084 - dense_3_loss_11: 0.0089 - dense_3_loss_12: 0.0084 - dense_3_loss_13: 0.0074 - dense_3_loss_14: 0.0079 - dense_3_loss_15: 0.0084 - dense_3_loss_16: 0.0088 - dense_3_loss_17: 0.0079 - dense_3_loss_18: 0.0079 - dense_3_loss_19: 0.0085 - dense_3_loss_20: 0.0088 - dense_3_loss_21: 0.0091 - dense_3_loss_22: 0.0085 - dense_3_loss_23: 0.0078 - dense_3_loss_24: 0.0079 - dense_3_loss_25: 0.0084 - dense_3_loss_26: 0.0082 - dense_3_loss_27: 0.0092 - dense_3_loss_28: 0.0098 - dense_3_loss_29: 0.0116 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 78/100
60/60 [==============================] - 1s - loss: 4.7843 - dense_3_loss_1: 3.5715 - dense_3_loss_2: 0.7886 - dense_3_loss_3: 0.1515 - dense_3_loss_4: 0.0344 - dense_3_loss_5: 0.0196 - dense_3_loss_6: 0.0157 - dense_3_loss_7: 0.0121 - dense_3_loss_8: 0.0110 - dense_3_loss_9: 0.0097 - dense_3_loss_10: 0.0083 - dense_3_loss_11: 0.0088 - dense_3_loss_12: 0.0084 - dense_3_loss_13: 0.0074 - dense_3_loss_14: 0.0078 - dense_3_loss_15: 0.0083 - dense_3_loss_16: 0.0087 - dense_3_loss_17: 0.0078 - dense_3_loss_18: 0.0078 - dense_3_loss_19: 0.0084 - dense_3_loss_20: 0.0087 - dense_3_loss_21: 0.0091 - dense_3_loss_22: 0.0084 - dense_3_loss_23: 0.0078 - dense_3_loss_24: 0.0079 - dense_3_loss_25: 0.0083 - dense_3_loss_26: 0.0081 - dense_3_loss_27: 0.0091 - dense_3_loss_28: 0.0097 - dense_3_loss_29: 0.0115 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 79/100
60/60 [==============================] - 1s - loss: 4.7773 - dense_3_loss_1: 3.5699 - dense_3_loss_2: 0.7863 - dense_3_loss_3: 0.1506 - dense_3_loss_4: 0.0341 - dense_3_loss_5: 0.0194 - dense_3_loss_6: 0.0155 - dense_3_loss_7: 0.0120 - dense_3_loss_8: 0.0109 - dense_3_loss_9: 0.0097 - dense_3_loss_10: 0.0082 - dense_3_loss_11: 0.0087 - dense_3_loss_12: 0.0083 - dense_3_loss_13: 0.0073 - dense_3_loss_14: 0.0078 - dense_3_loss_15: 0.0082 - dense_3_loss_16: 0.0086 - dense_3_loss_17: 0.0077 - dense_3_loss_18: 0.0077 - dense_3_loss_19: 0.0083 - dense_3_loss_20: 0.0086 - dense_3_loss_21: 0.0090 - dense_3_loss_22: 0.0084 - dense_3_loss_23: 0.0077 - dense_3_loss_24: 0.0078 - dense_3_loss_25: 0.0082 - dense_3_loss_26: 0.0080 - dense_3_loss_27: 0.0090 - dense_3_loss_28: 0.0097 - dense_3_loss_29: 0.0114 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 80/100
60/60 [==============================] - 1s - loss: 4.7714 - dense_3_loss_1: 3.5683 - dense_3_loss_2: 0.7843 - dense_3_loss_3: 0.1502 - dense_3_loss_4: 0.0339 - dense_3_loss_5: 0.0193 - dense_3_loss_6: 0.0154 - dense_3_loss_7: 0.0119 - dense_3_loss_8: 0.0108 - dense_3_loss_9: 0.0096 - dense_3_loss_10: 0.0082 - dense_3_loss_11: 0.0087 - dense_3_loss_12: 0.0082 - dense_3_loss_13: 0.0072 - dense_3_loss_14: 0.0077 - dense_3_loss_15: 0.0082 - dense_3_loss_16: 0.0086 - dense_3_loss_17: 0.0077 - dense_3_loss_18: 0.0077 - dense_3_loss_19: 0.0083 - dense_3_loss_20: 0.0086 - dense_3_loss_21: 0.0089 - dense_3_loss_22: 0.0083 - dense_3_loss_23: 0.0077 - dense_3_loss_24: 0.0077 - dense_3_loss_25: 0.0082 - dense_3_loss_26: 0.0080 - dense_3_loss_27: 0.0090 - dense_3_loss_28: 0.0096 - dense_3_loss_29: 0.0113 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 81/100
60/60 [==============================] - 1s - loss: 4.7639 - dense_3_loss_1: 3.5668 - dense_3_loss_2: 0.7815 - dense_3_loss_3: 0.1493 - dense_3_loss_4: 0.0335 - dense_3_loss_5: 0.0192 - dense_3_loss_6: 0.0153 - dense_3_loss_7: 0.0118 - dense_3_loss_8: 0.0107 - dense_3_loss_9: 0.0095 - dense_3_loss_10: 0.0081 - dense_3_loss_11: 0.0086 - dense_3_loss_12: 0.0082 - dense_3_loss_13: 0.0072 - dense_3_loss_14: 0.0076 - dense_3_loss_15: 0.0081 - dense_3_loss_16: 0.0085 - dense_3_loss_17: 0.0076 - dense_3_loss_18: 0.0076 - dense_3_loss_19: 0.0082 - dense_3_loss_20: 0.0085 - dense_3_loss_21: 0.0088 - dense_3_loss_22: 0.0082 - dense_3_loss_23: 0.0076 - dense_3_loss_24: 0.0077 - dense_3_loss_25: 0.0081 - dense_3_loss_26: 0.0079 - dense_3_loss_27: 0.0089 - dense_3_loss_28: 0.0095 - dense_3_loss_29: 0.0112 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 82/100
60/60 [==============================] - 1s - loss: 4.7576 - dense_3_loss_1: 3.5652 - dense_3_loss_2: 0.7794 - dense_3_loss_3: 0.1487 - dense_3_loss_4: 0.0333 - dense_3_loss_5: 0.0191 - dense_3_loss_6: 0.0152 - dense_3_loss_7: 0.0117 - dense_3_loss_8: 0.0106 - dense_3_loss_9: 0.0095 - dense_3_loss_10: 0.0081 - dense_3_loss_11: 0.0086 - dense_3_loss_12: 0.0081 - dense_3_loss_13: 0.0071 - dense_3_loss_14: 0.0076 - dense_3_loss_15: 0.0081 - dense_3_loss_16: 0.0084 - dense_3_loss_17: 0.0076 - dense_3_loss_18: 0.0076 - dense_3_loss_19: 0.0081 - dense_3_loss_20: 0.0084 - dense_3_loss_21: 0.0088 - dense_3_loss_22: 0.0082 - dense_3_loss_23: 0.0075 - dense_3_loss_24: 0.0076 - dense_3_loss_25: 0.0081 - dense_3_loss_26: 0.0078 - dense_3_loss_27: 0.0088 - dense_3_loss_28: 0.0094 - dense_3_loss_29: 0.0111 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 83/100
60/60 [==============================] - 1s - loss: 4.7514 - dense_3_loss_1: 3.5637 - dense_3_loss_2: 0.7773 - dense_3_loss_3: 0.1482 - dense_3_loss_4: 0.0330 - dense_3_loss_5: 0.0189 - dense_3_loss_6: 0.0150 - dense_3_loss_7: 0.0116 - dense_3_loss_8: 0.0105 - dense_3_loss_9: 0.0094 - dense_3_loss_10: 0.0080 - dense_3_loss_11: 0.0085 - dense_3_loss_12: 0.0080 - dense_3_loss_13: 0.0071 - dense_3_loss_14: 0.0075 - dense_3_loss_15: 0.0080 - dense_3_loss_16: 0.0084 - dense_3_loss_17: 0.0075 - dense_3_loss_18: 0.0075 - dense_3_loss_19: 0.0081 - dense_3_loss_20: 0.0084 - dense_3_loss_21: 0.0087 - dense_3_loss_22: 0.0081 - dense_3_loss_23: 0.0075 - dense_3_loss_24: 0.0076 - dense_3_loss_25: 0.0080 - dense_3_loss_26: 0.0078 - dense_3_loss_27: 0.0088 - dense_3_loss_28: 0.0094 - dense_3_loss_29: 0.0111 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 84/100
60/60 [==============================] - 1s - loss: 4.7455 - dense_3_loss_1: 3.5622 - dense_3_loss_2: 0.7752 - dense_3_loss_3: 0.1478 - dense_3_loss_4: 0.0328 - dense_3_loss_5: 0.0188 - dense_3_loss_6: 0.0149 - dense_3_loss_7: 0.0115 - dense_3_loss_8: 0.0105 - dense_3_loss_9: 0.0093 - dense_3_loss_10: 0.0079 - dense_3_loss_11: 0.0084 - dense_3_loss_12: 0.0080 - dense_3_loss_13: 0.0070 - dense_3_loss_14: 0.0075 - dense_3_loss_15: 0.0079 - dense_3_loss_16: 0.0083 - dense_3_loss_17: 0.0074 - dense_3_loss_18: 0.0075 - dense_3_loss_19: 0.0080 - dense_3_loss_20: 0.0083 - dense_3_loss_21: 0.0086 - dense_3_loss_22: 0.0080 - dense_3_loss_23: 0.0074 - dense_3_loss_24: 0.0075 - dense_3_loss_25: 0.0079 - dense_3_loss_26: 0.0077 - dense_3_loss_27: 0.0087 - dense_3_loss_28: 0.0093 - dense_3_loss_29: 0.0110 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 85/100
60/60 [==============================] - 1s - loss: 4.7395 - dense_3_loss_1: 3.5607 - dense_3_loss_2: 0.7734 - dense_3_loss_3: 0.1470 - dense_3_loss_4: 0.0326 - dense_3_loss_5: 0.0186 - dense_3_loss_6: 0.0148 - dense_3_loss_7: 0.0114 - dense_3_loss_8: 0.0104 - dense_3_loss_9: 0.0092 - dense_3_loss_10: 0.0079 - dense_3_loss_11: 0.0083 - dense_3_loss_12: 0.0079 - dense_3_loss_13: 0.0070 - dense_3_loss_14: 0.0074 - dense_3_loss_15: 0.0079 - dense_3_loss_16: 0.0082 - dense_3_loss_17: 0.0074 - dense_3_loss_18: 0.0074 - dense_3_loss_19: 0.0080 - dense_3_loss_20: 0.0082 - dense_3_loss_21: 0.0086 - dense_3_loss_22: 0.0080 - dense_3_loss_23: 0.0074 - dense_3_loss_24: 0.0075 - dense_3_loss_25: 0.0079 - dense_3_loss_26: 0.0077 - dense_3_loss_27: 0.0086 - dense_3_loss_28: 0.0092 - dense_3_loss_29: 0.0109 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7167 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 86/100
60/60 [==============================] - 1s - loss: 4.7329 - dense_3_loss_1: 3.5590 - dense_3_loss_2: 0.7711 - dense_3_loss_3: 0.1462 - dense_3_loss_4: 0.0323 - dense_3_loss_5: 0.0185 - dense_3_loss_6: 0.0147 - dense_3_loss_7: 0.0114 - dense_3_loss_8: 0.0103 - dense_3_loss_9: 0.0092 - dense_3_loss_10: 0.0078 - dense_3_loss_11: 0.0083 - dense_3_loss_12: 0.0079 - dense_3_loss_13: 0.0069 - dense_3_loss_14: 0.0074 - dense_3_loss_15: 0.0078 - dense_3_loss_16: 0.0082 - dense_3_loss_17: 0.0073 - dense_3_loss_18: 0.0073 - dense_3_loss_19: 0.0079 - dense_3_loss_20: 0.0082 - dense_3_loss_21: 0.0085 - dense_3_loss_22: 0.0079 - dense_3_loss_23: 0.0073 - dense_3_loss_24: 0.0074 - dense_3_loss_25: 0.0078 - dense_3_loss_26: 0.0076 - dense_3_loss_27: 0.0086 - dense_3_loss_28: 0.0092 - dense_3_loss_29: 0.0108 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 87/100
60/60 [==============================] - 1s - loss: 4.7268 - dense_3_loss_1: 3.5576 - dense_3_loss_2: 0.7690 - dense_3_loss_3: 0.1456 - dense_3_loss_4: 0.0321 - dense_3_loss_5: 0.0184 - dense_3_loss_6: 0.0146 - dense_3_loss_7: 0.0113 - dense_3_loss_8: 0.0102 - dense_3_loss_9: 0.0091 - dense_3_loss_10: 0.0078 - dense_3_loss_11: 0.0082 - dense_3_loss_12: 0.0078 - dense_3_loss_13: 0.0069 - dense_3_loss_14: 0.0073 - dense_3_loss_15: 0.0078 - dense_3_loss_16: 0.0081 - dense_3_loss_17: 0.0073 - dense_3_loss_18: 0.0073 - dense_3_loss_19: 0.0078 - dense_3_loss_20: 0.0081 - dense_3_loss_21: 0.0084 - dense_3_loss_22: 0.0079 - dense_3_loss_23: 0.0073 - dense_3_loss_24: 0.0074 - dense_3_loss_25: 0.0077 - dense_3_loss_26: 0.0075 - dense_3_loss_27: 0.0085 - dense_3_loss_28: 0.0091 - dense_3_loss_29: 0.0107 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 88/100
60/60 [==============================] - 1s - loss: 4.7209 - dense_3_loss_1: 3.5561 - dense_3_loss_2: 0.7671 - dense_3_loss_3: 0.1450 - dense_3_loss_4: 0.0319 - dense_3_loss_5: 0.0183 - dense_3_loss_6: 0.0145 - dense_3_loss_7: 0.0112 - dense_3_loss_8: 0.0102 - dense_3_loss_9: 0.0090 - dense_3_loss_10: 0.0077 - dense_3_loss_11: 0.0081 - dense_3_loss_12: 0.0077 - dense_3_loss_13: 0.0068 - dense_3_loss_14: 0.0073 - dense_3_loss_15: 0.0077 - dense_3_loss_16: 0.0081 - dense_3_loss_17: 0.0072 - dense_3_loss_18: 0.0072 - dense_3_loss_19: 0.0078 - dense_3_loss_20: 0.0081 - dense_3_loss_21: 0.0084 - dense_3_loss_22: 0.0078 - dense_3_loss_23: 0.0072 - dense_3_loss_24: 0.0073 - dense_3_loss_25: 0.0077 - dense_3_loss_26: 0.0075 - dense_3_loss_27: 0.0084 - dense_3_loss_28: 0.0090 - dense_3_loss_29: 0.0106 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500         
Epoch 89/100
60/60 [==============================] - 1s - loss: 4.7154 - dense_3_loss_1: 3.5545 - dense_3_loss_2: 0.7651 - dense_3_loss_3: 0.1448 - dense_3_loss_4: 0.0317 - dense_3_loss_5: 0.0181 - dense_3_loss_6: 0.0144 - dense_3_loss_7: 0.0111 - dense_3_loss_8: 0.0101 - dense_3_loss_9: 0.0090 - dense_3_loss_10: 0.0077 - dense_3_loss_11: 0.0081 - dense_3_loss_12: 0.0077 - dense_3_loss_13: 0.0068 - dense_3_loss_14: 0.0072 - dense_3_loss_15: 0.0076 - dense_3_loss_16: 0.0080 - dense_3_loss_17: 0.0072 - dense_3_loss_18: 0.0072 - dense_3_loss_19: 0.0077 - dense_3_loss_20: 0.0080 - dense_3_loss_21: 0.0083 - dense_3_loss_22: 0.0078 - dense_3_loss_23: 0.0072 - dense_3_loss_24: 0.0073 - dense_3_loss_25: 0.0076 - dense_3_loss_26: 0.0074 - dense_3_loss_27: 0.0084 - dense_3_loss_28: 0.0090 - dense_3_loss_29: 0.0105 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 90/100
60/60 [==============================] - 1s - loss: 4.7096 - dense_3_loss_1: 3.5533 - dense_3_loss_2: 0.7631 - dense_3_loss_3: 0.1440 - dense_3_loss_4: 0.0315 - dense_3_loss_5: 0.0180 - dense_3_loss_6: 0.0143 - dense_3_loss_7: 0.0110 - dense_3_loss_8: 0.0100 - dense_3_loss_9: 0.0089 - dense_3_loss_10: 0.0076 - dense_3_loss_11: 0.0080 - dense_3_loss_12: 0.0076 - dense_3_loss_13: 0.0067 - dense_3_loss_14: 0.0071 - dense_3_loss_15: 0.0076 - dense_3_loss_16: 0.0079 - dense_3_loss_17: 0.0071 - dense_3_loss_18: 0.0071 - dense_3_loss_19: 0.0077 - dense_3_loss_20: 0.0080 - dense_3_loss_21: 0.0083 - dense_3_loss_22: 0.0077 - dense_3_loss_23: 0.0071 - dense_3_loss_24: 0.0072 - dense_3_loss_25: 0.0076 - dense_3_loss_26: 0.0074 - dense_3_loss_27: 0.0083 - dense_3_loss_28: 0.0089 - dense_3_loss_29: 0.0105 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 91/100
60/60 [==============================] - 1s - loss: 4.7036 - dense_3_loss_1: 3.5517 - dense_3_loss_2: 0.7612 - dense_3_loss_3: 0.1432 - dense_3_loss_4: 0.0313 - dense_3_loss_5: 0.0179 - dense_3_loss_6: 0.0141 - dense_3_loss_7: 0.0110 - dense_3_loss_8: 0.0100 - dense_3_loss_9: 0.0089 - dense_3_loss_10: 0.0076 - dense_3_loss_11: 0.0080 - dense_3_loss_12: 0.0076 - dense_3_loss_13: 0.0067 - dense_3_loss_14: 0.0071 - dense_3_loss_15: 0.0075 - dense_3_loss_16: 0.0079 - dense_3_loss_17: 0.0071 - dense_3_loss_18: 0.0071 - dense_3_loss_19: 0.0076 - dense_3_loss_20: 0.0079 - dense_3_loss_21: 0.0082 - dense_3_loss_22: 0.0076 - dense_3_loss_23: 0.0071 - dense_3_loss_24: 0.0072 - dense_3_loss_25: 0.0075 - dense_3_loss_26: 0.0073 - dense_3_loss_27: 0.0082 - dense_3_loss_28: 0.0088 - dense_3_loss_29: 0.0104 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 92/100
60/60 [==============================] - 1s - loss: 4.6980 - dense_3_loss_1: 3.5503 - dense_3_loss_2: 0.7593 - dense_3_loss_3: 0.1428 - dense_3_loss_4: 0.0310 - dense_3_loss_5: 0.0178 - dense_3_loss_6: 0.0141 - dense_3_loss_7: 0.0109 - dense_3_loss_8: 0.0099 - dense_3_loss_9: 0.0088 - dense_3_loss_10: 0.0075 - dense_3_loss_11: 0.0079 - dense_3_loss_12: 0.0075 - dense_3_loss_13: 0.0066 - dense_3_loss_14: 0.0070 - dense_3_loss_15: 0.0075 - dense_3_loss_16: 0.0078 - dense_3_loss_17: 0.0070 - dense_3_loss_18: 0.0070 - dense_3_loss_19: 0.0076 - dense_3_loss_20: 0.0078 - dense_3_loss_21: 0.0081 - dense_3_loss_22: 0.0076 - dense_3_loss_23: 0.0070 - dense_3_loss_24: 0.0071 - dense_3_loss_25: 0.0075 - dense_3_loss_26: 0.0073 - dense_3_loss_27: 0.0082 - dense_3_loss_28: 0.0088 - dense_3_loss_29: 0.0103 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 93/100
60/60 [==============================] - 1s - loss: 4.6924 - dense_3_loss_1: 3.5489 - dense_3_loss_2: 0.7574 - dense_3_loss_3: 0.1423 - dense_3_loss_4: 0.0308 - dense_3_loss_5: 0.0177 - dense_3_loss_6: 0.0140 - dense_3_loss_7: 0.0108 - dense_3_loss_8: 0.0098 - dense_3_loss_9: 0.0088 - dense_3_loss_10: 0.0074 - dense_3_loss_11: 0.0079 - dense_3_loss_12: 0.0075 - dense_3_loss_13: 0.0066 - dense_3_loss_14: 0.0070 - dense_3_loss_15: 0.0074 - dense_3_loss_16: 0.0078 - dense_3_loss_17: 0.0070 - dense_3_loss_18: 0.0070 - dense_3_loss_19: 0.0075 - dense_3_loss_20: 0.0078 - dense_3_loss_21: 0.0081 - dense_3_loss_22: 0.0075 - dense_3_loss_23: 0.0070 - dense_3_loss_24: 0.0071 - dense_3_loss_25: 0.0074 - dense_3_loss_26: 0.0072 - dense_3_loss_27: 0.0081 - dense_3_loss_28: 0.0087 - dense_3_loss_29: 0.0102 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 94/100
60/60 [==============================] - 1s - loss: 4.6873 - dense_3_loss_1: 3.5474 - dense_3_loss_2: 0.7558 - dense_3_loss_3: 0.1418 - dense_3_loss_4: 0.0306 - dense_3_loss_5: 0.0176 - dense_3_loss_6: 0.0139 - dense_3_loss_7: 0.0107 - dense_3_loss_8: 0.0097 - dense_3_loss_9: 0.0087 - dense_3_loss_10: 0.0074 - dense_3_loss_11: 0.0078 - dense_3_loss_12: 0.0074 - dense_3_loss_13: 0.0065 - dense_3_loss_14: 0.0069 - dense_3_loss_15: 0.0074 - dense_3_loss_16: 0.0077 - dense_3_loss_17: 0.0069 - dense_3_loss_18: 0.0069 - dense_3_loss_19: 0.0075 - dense_3_loss_20: 0.0077 - dense_3_loss_21: 0.0080 - dense_3_loss_22: 0.0075 - dense_3_loss_23: 0.0069 - dense_3_loss_24: 0.0070 - dense_3_loss_25: 0.0074 - dense_3_loss_26: 0.0071 - dense_3_loss_27: 0.0081 - dense_3_loss_28: 0.0086 - dense_3_loss_29: 0.0102 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 95/100
60/60 [==============================] - 1s - loss: 4.6816 - dense_3_loss_1: 3.5459 - dense_3_loss_2: 0.7536 - dense_3_loss_3: 0.1416 - dense_3_loss_4: 0.0303 - dense_3_loss_5: 0.0174 - dense_3_loss_6: 0.0138 - dense_3_loss_7: 0.0106 - dense_3_loss_8: 0.0097 - dense_3_loss_9: 0.0087 - dense_3_loss_10: 0.0073 - dense_3_loss_11: 0.0078 - dense_3_loss_12: 0.0074 - dense_3_loss_13: 0.0065 - dense_3_loss_14: 0.0069 - dense_3_loss_15: 0.0073 - dense_3_loss_16: 0.0077 - dense_3_loss_17: 0.0069 - dense_3_loss_18: 0.0069 - dense_3_loss_19: 0.0074 - dense_3_loss_20: 0.0077 - dense_3_loss_21: 0.0080 - dense_3_loss_22: 0.0074 - dense_3_loss_23: 0.0069 - dense_3_loss_24: 0.0070 - dense_3_loss_25: 0.0073 - dense_3_loss_26: 0.0071 - dense_3_loss_27: 0.0080 - dense_3_loss_28: 0.0086 - dense_3_loss_29: 0.0101 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 96/100
60/60 [==============================] - 1s - loss: 4.6764 - dense_3_loss_1: 3.5446 - dense_3_loss_2: 0.7521 - dense_3_loss_3: 0.1409 - dense_3_loss_4: 0.0301 - dense_3_loss_5: 0.0173 - dense_3_loss_6: 0.0137 - dense_3_loss_7: 0.0106 - dense_3_loss_8: 0.0096 - dense_3_loss_9: 0.0086 - dense_3_loss_10: 0.0073 - dense_3_loss_11: 0.0077 - dense_3_loss_12: 0.0073 - dense_3_loss_13: 0.0065 - dense_3_loss_14: 0.0068 - dense_3_loss_15: 0.0073 - dense_3_loss_16: 0.0076 - dense_3_loss_17: 0.0068 - dense_3_loss_18: 0.0068 - dense_3_loss_19: 0.0074 - dense_3_loss_20: 0.0076 - dense_3_loss_21: 0.0079 - dense_3_loss_22: 0.0074 - dense_3_loss_23: 0.0068 - dense_3_loss_24: 0.0069 - dense_3_loss_25: 0.0073 - dense_3_loss_26: 0.0070 - dense_3_loss_27: 0.0080 - dense_3_loss_28: 0.0085 - dense_3_loss_29: 0.0100 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 97/100
60/60 [==============================] - 1s - loss: 4.6714 - dense_3_loss_1: 3.5432 - dense_3_loss_2: 0.7503 - dense_3_loss_3: 0.1408 - dense_3_loss_4: 0.0299 - dense_3_loss_5: 0.0172 - dense_3_loss_6: 0.0136 - dense_3_loss_7: 0.0105 - dense_3_loss_8: 0.0095 - dense_3_loss_9: 0.0086 - dense_3_loss_10: 0.0072 - dense_3_loss_11: 0.0077 - dense_3_loss_12: 0.0072 - dense_3_loss_13: 0.0064 - dense_3_loss_14: 0.0068 - dense_3_loss_15: 0.0072 - dense_3_loss_16: 0.0076 - dense_3_loss_17: 0.0068 - dense_3_loss_18: 0.0068 - dense_3_loss_19: 0.0073 - dense_3_loss_20: 0.0075 - dense_3_loss_21: 0.0078 - dense_3_loss_22: 0.0073 - dense_3_loss_23: 0.0068 - dense_3_loss_24: 0.0069 - dense_3_loss_25: 0.0072 - dense_3_loss_26: 0.0070 - dense_3_loss_27: 0.0079 - dense_3_loss_28: 0.0085 - dense_3_loss_29: 0.0099 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 98/100
60/60 [==============================] - 1s - loss: 4.6657 - dense_3_loss_1: 3.5418 - dense_3_loss_2: 0.7484 - dense_3_loss_3: 0.1400 - dense_3_loss_4: 0.0298 - dense_3_loss_5: 0.0171 - dense_3_loss_6: 0.0135 - dense_3_loss_7: 0.0104 - dense_3_loss_8: 0.0095 - dense_3_loss_9: 0.0085 - dense_3_loss_10: 0.0072 - dense_3_loss_11: 0.0076 - dense_3_loss_12: 0.0072 - dense_3_loss_13: 0.0064 - dense_3_loss_14: 0.0067 - dense_3_loss_15: 0.0072 - dense_3_loss_16: 0.0075 - dense_3_loss_17: 0.0067 - dense_3_loss_18: 0.0067 - dense_3_loss_19: 0.0073 - dense_3_loss_20: 0.0075 - dense_3_loss_21: 0.0078 - dense_3_loss_22: 0.0073 - dense_3_loss_23: 0.0067 - dense_3_loss_24: 0.0068 - dense_3_loss_25: 0.0072 - dense_3_loss_26: 0.0069 - dense_3_loss_27: 0.0078 - dense_3_loss_28: 0.0084 - dense_3_loss_29: 0.0099 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 99/100
60/60 [==============================] - 1s - loss: 4.6606 - dense_3_loss_1: 3.5405 - dense_3_loss_2: 0.7468 - dense_3_loss_3: 0.1394 - dense_3_loss_4: 0.0296 - dense_3_loss_5: 0.0170 - dense_3_loss_6: 0.0134 - dense_3_loss_7: 0.0104 - dense_3_loss_8: 0.0094 - dense_3_loss_9: 0.0084 - dense_3_loss_10: 0.0071 - dense_3_loss_11: 0.0076 - dense_3_loss_12: 0.0072 - dense_3_loss_13: 0.0063 - dense_3_loss_14: 0.0067 - dense_3_loss_15: 0.0071 - dense_3_loss_16: 0.0075 - dense_3_loss_17: 0.0067 - dense_3_loss_18: 0.0067 - dense_3_loss_19: 0.0072 - dense_3_loss_20: 0.0074 - dense_3_loss_21: 0.0077 - dense_3_loss_22: 0.0072 - dense_3_loss_23: 0.0067 - dense_3_loss_24: 0.0068 - dense_3_loss_25: 0.0071 - dense_3_loss_26: 0.0069 - dense_3_loss_27: 0.0078 - dense_3_loss_28: 0.0083 - dense_3_loss_29: 0.0098 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     
Epoch 100/100
60/60 [==============================] - 1s - loss: 4.6554 - dense_3_loss_1: 3.5392 - dense_3_loss_2: 0.7451 - dense_3_loss_3: 0.1388 - dense_3_loss_4: 0.0294 - dense_3_loss_5: 0.0169 - dense_3_loss_6: 0.0133 - dense_3_loss_7: 0.0103 - dense_3_loss_8: 0.0093 - dense_3_loss_9: 0.0084 - dense_3_loss_10: 0.0071 - dense_3_loss_11: 0.0075 - dense_3_loss_12: 0.0071 - dense_3_loss_13: 0.0063 - dense_3_loss_14: 0.0067 - dense_3_loss_15: 0.0071 - dense_3_loss_16: 0.0074 - dense_3_loss_17: 0.0066 - dense_3_loss_18: 0.0066 - dense_3_loss_19: 0.0072 - dense_3_loss_20: 0.0074 - dense_3_loss_21: 0.0077 - dense_3_loss_22: 0.0072 - dense_3_loss_23: 0.0066 - dense_3_loss_24: 0.0067 - dense_3_loss_25: 0.0071 - dense_3_loss_26: 0.0068 - dense_3_loss_27: 0.0077 - dense_3_loss_28: 0.0083 - dense_3_loss_29: 0.0097 - dense_3_loss_30: 0.0000e+00 - dense_3_acc_1: 0.1000 - dense_3_acc_2: 0.7333 - dense_3_acc_3: 0.9667 - dense_3_acc_4: 1.0000 - dense_3_acc_5: 1.0000 - dense_3_acc_6: 1.0000 - dense_3_acc_7: 1.0000 - dense_3_acc_8: 1.0000 - dense_3_acc_9: 1.0000 - dense_3_acc_10: 1.0000 - dense_3_acc_11: 1.0000 - dense_3_acc_12: 1.0000 - dense_3_acc_13: 1.0000 - dense_3_acc_14: 1.0000 - dense_3_acc_15: 1.0000 - dense_3_acc_16: 1.0000 - dense_3_acc_17: 1.0000 - dense_3_acc_18: 1.0000 - dense_3_acc_19: 1.0000 - dense_3_acc_20: 1.0000 - dense_3_acc_21: 1.0000 - dense_3_acc_22: 1.0000 - dense_3_acc_23: 1.0000 - dense_3_acc_24: 1.0000 - dense_3_acc_25: 1.0000 - dense_3_acc_26: 1.0000 - dense_3_acc_27: 1.0000 - dense_3_acc_28: 1.0000 - dense_3_acc_29: 1.0000 - dense_3_acc_30: 0.0500     





<keras.callbacks.History at 0x7f7fa8a51ac8>

You should see the model loss going down. Now that you have trained a model, lets go on the the final section to implement an inference algorithm, and generate some music!

3 - Generating music

You now have a trained model which has learned the patterns of the jazz soloist. Lets now use this model to synthesize new music.

3.1 - Predicting & Sampling

爵士乐生成

At each step of sampling, you will take as input the activation a and cell state c from the previous state of the LSTM, forward propagate by one step, and get a new output activation as well as cell state. The new activation a can then be used to generate the output, using densor as before.

To start off the model, we will initialize x0 as well as the LSTM activation and and cell value a0 and c0 to be zeros.

Exercise:Implement the function below to sample a sequence of musical values. Here are some of the key steps you’ll need to implement inside the for-loop that generates the $T_y$ output characters:

Step 2.A: Use LSTM_Cell , which inputs the previous step’s c and a to generate the current step’s c and a .

Step 2.B: Use densor (defined previously) to compute a softmax on a to get the output for the current step.

Step 2.C: Save the output you have just generated by appending it to outputs .

Step 2.D: Sample x to the be “out”‘s one-hot version (the prediction) so that you can pass it to the next LSTM’s step. We have already provided this line of code, which uses a Lambda function.

x = Lambda(one_hot)(out)

[Minor technical note: Rather than sampling a value at random according to the probabilities in out , this line of code actually chooses the single most likely note at each step using an argmax.]

# GRADED FUNCTION: music_inference_model

def music_inference_model(LSTM_cell, densor, n_values =78, n_a =64, Ty =100):
    """
Uses the trained "LSTM_cell" and "densor" from model() to generate a sequence of values.

Arguments:
LSTM_cell -- the trained "LSTM_cell" from model(), Keras layer object
densor -- the trained "densor" from model(), Keras layer object
n_values -- integer, umber of unique values
n_a -- number of units in the LSTM_cell
Ty -- integer, number of time steps to generate

Returns:
inference_model -- Keras model instance
"""
    
    # Define the input of your model with a shape
    x0 = Input(shape=(1, n_values))
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    x = x0

    ### START CODE HERE ###
    # Step 1: Create an empty list of "outputs" to later store your predicted values (≈1 line)
    outputs = list()
    
    # Step 2: Loop over Ty and generate a value at every time step
    for t in range(Ty):
        
        # Step 2.A: Perform one step of LSTM_cell (≈1 line)
        a, _, c = LSTM_cell(x, initial_state=[a, c])
        
        # Step 2.B: Apply Dense layer to the hidden state output of the LSTM_cell (≈1 line)
        out = densor(a)

        # Step 2.C: Append the prediction "out" to "outputs". out.shape = (None, 78) (≈1 line)
        outputs.append(out)
        
        # Step 2.D: Select the next value according to "out", and set "x" to be the one-hot representation of the
        # selected value, which will be passed as the input to LSTM_cell on the next step. We have provided
        # the line of code you need to do this.
        x = Lambda(one_hot)(out)
        
    # Step 3: Create model instance with the correct "inputs" and "outputs" (≈1 line)
    inference_model = Model(inputs=[x0, a0, c0], outputs=outputs)
    
    ### END CODE HERE ###
    
    return inference_model

Run the cell below to define your inference model. This model is hard coded to generate 50 values.

inference_model = music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 50)

Finally, this creates the zero-valued vectors you will use to initialize x and the LSTM state variables a and c .

x_initializer = np.zeros((1, 1, 78))
a_initializer = np.zeros((1, n_a))
c_initializer = np.zeros((1, n_a))

Exercise: Implement predict_and_sample() . This function takes many arguments including the inputs [x_initializer, a_initializer, c_initializer]. In order to predict the output corresponding to this input, you will need to carry-out 3 steps:

Use your inference model to predict an output given your set of inputs. The output pred should be a list of length $T_y$ where each element is a numpy-array of shape (1, n_values).
Convert pred into a numpy array of $T_y$ indices. Each index corresponds is computed by taking the argmax of an element of the pred list. Hint .
Convert the indices into their one-hot vector representations. Hint .

# GRADED FUNCTION: predict_and_sample

def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer,
c_initializer = c_initializer):
    """
Predicts the next value of values using the inference model.

Arguments:
inference_model -- Keras model instance for inference time
x_initializer -- numpy array of shape (1, 1, 78), one-hot vector initializing the values generation
a_initializer -- numpy array of shape (1, n_a), initializing the hidden state of the LSTM_cell
c_initializer -- numpy array of shape (1, n_a), initializing the cell state of the LSTM_cel

Returns:
results -- numpy-array of shape (Ty, 78), matrix of one-hot vectors representing the values generated
indices -- numpy-array of shape (Ty, 1), matrix of indices representing the values generated
"""
    
    ### START CODE HERE ###
    # Step 1: Use your inference model to predict an output sequence given x_initializer, a_initializer and c_initializer.
    pred = inference_model.predict([x_initializer, a_initializer, c_initializer])
    # Step 2: Convert "pred" into an np.array() of indices with the maximum probabilities
    indices = np.argmax(np.array(pred), axis=-1)
    # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, )
    results = to_categorical(indices)
    ### END CODE HERE ###
    
    return results, indices

results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)
print("np.argmax(results[12]) =", np.argmax(results[12]))
print("np.argmax(results[17]) =", np.argmax(results[17]))
print("list(indices[12:18]) =", list(indices[12:18]))

np.argmax(results[12]) = 61
np.argmax(results[17]) = 4
list(indices[12:18]) = [array([61]), array([32]), array([33]), array([20]), array([24]), array([4])]

Expected Output: Your results may differ because Keras’ results are not completely predictable. However, if you have trained your LSTM_cell with model.fit() for exactly 100 epochs as described above, you should very likely observe a sequence of indices that are not all identical. Moreover, you should observe that: np.argmax(results[12]) is the first element of list(indices[12:18]) and np.argmax(results[17]) is the last element of list(indices[12:18]).

np.argmax(results[12]) =

list(indices[12:18]) =

[array([1]), array([42]), array([54]), array([17]), array([1]), array([42])]

3.3 - Generate music

Finally, you are ready to generate music. Your RNN generates a sequence of values. The following code generates music by first calling your predict_and_sample() function. These values are then post-processed into musical chords (meaning that multiple values or notes can be played at the same time).

Most computational music algorithms use some post-processing because it is difficult to generate music that sounds good without such post-processing. The post-processing does things such as clean up the generated audio by making sure the same sound is not repeated too many times, that two successive notes are not too far from each other in pitch, and so on. One could argue that a lot of these post-processing steps are hacks; also, a lot the music generation literature has also focused on hand-crafting post-processors, and a lot of the output quality depends on the quality of the post-processing and not just the quality of the RNN. But this post-processing does make a huge difference, so lets use it in our implementation as well.

Lets make some music!

Run the following cell to generate music and record it into your out_stream . This can take a couple of minutes.

out_stream = generate_music(inference_model)

Predicting new values for different set of chords.
Generated 51 sounds using the predicted values for the set of chords ("1") and after pruning
Generated 51 sounds using the predicted values for the set of chords ("2") and after pruning
Generated 51 sounds using the predicted values for the set of chords ("3") and after pruning
Generated 50 sounds using the predicted values for the set of chords ("4") and after pruning
Generated 51 sounds using the predicted values for the set of chords ("5") and after pruning
Your generated music is saved in output/my_music.midi

To listen to your music, click File->Open… Then go to “output/“ and download “my_music.midi”. Either play it on your computer with an application that can read midi files if you have one, or use one of the free online “MIDI to mp3” conversion tools to convert this to mp3.

As reference, here also is a 30sec audio clip we generated using this algorithm.

IPython.display.Audio('./data/30s_trained_model.mp3')

Congratulations!

You have come to the end of the notebook.

Here’s what you should remember:

A sequence model can be used to generate musical values, which are then post-processed into midi music.
Fairly similar models can be used to generate dinosaur names or to generate music, with the major difference being the input fed to the model.
In Keras, sequence generation involves defining layers with shared weights, which are then repeated for the different time steps $1, \ldots, T_x$.

Congratulations on completing this assignment and generating a jazz solo!

References

The ideas presented in this notebook came primarily from three computational music papers cited below. The implementation here also took significant inspiration and used many components from Ji-Sung Kim’s github repository.

Ji-Sung Kim, 2016, deepjazz
Jon Gillick, Kevin Tang and Robert Keller, 2009. Learning Jazz Grammars
Robert Keller and David Morrison, 2007, A Grammatical Approach to Automatic Improvisation
François Pachet, 1999, Surprising Harmonies

We’re also grateful to François Germain for valuable feedback.

以上就是本文的全部内容，希望本文的内容对大家的学习或者工作能带来一定的帮助，也希望大家多多支持码农网

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罗辑思维

罗振宇 / 长江文艺出版社 / 2013-10-25 / 36.00

本书根据罗振宇的互联网视频知识脱口秀《罗辑思维》创作。资深媒体人罗振宇对正在到来的互联网时代有深刻的洞察。他认为，互联网正在成为我们生活中的“基础设施”，它将彻底改变人类协作的方式，使组织逐渐瓦解、消融，而个体生命的自由价值得到充分释放。《罗辑思维》的口号是“有种、有趣、有料”，做大家“身边的读书人”，倡导独立、理性的思考，凝聚爱智求真、积极上进、自由阳光、人格健全的年轻人。......一起来看看《罗辑思维》这本书的介绍吧!

码农工具

爵士乐生成

Improvise a Jazz Solo with an LSTM Network

You will learn to:

1 - Problem statement

1.1 - Dataset

1.2 - Overview of our model

2 - Building the model

3 - Generating music

3.1 - Predicting & Sampling

3.3 - Generate music

Congratulations!

References

罗辑思维

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