用LSTMNetwork to create a small jazz songs

在本次作業中,你將使用LSTMImplementation model which generates the music.You can listen at the end of the operation of their own creation music.
你將學習：

• 將LSTMApplied to music generated.
• Through the deep learning generate your own jazz music.

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

Using TensorFlow backend.

1 問題陳述

You to the special composing a jazz music for your friend's birthday.但是,You don't know any instrument or music works.幸運的是,You know deep learning and can be usedLSTMNetwork to try to solve this problem.

You will be training a network,To show the style of work to generate novel small jazz songs.

1.1 數據集

You will be training algorithm in jazz music corpus.Run the following cell to audition training focused audio clips：

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

由於CSDNCan't show the music,Blogger would not be in this show.

We have music data preprocessing,Based on the music“value”Presents the music data.你可以將每個“值”As a note,Including the duration of a tone and a.例如,If you press the specific piano keys0.5秒鐘,Are you just played a note.在音樂理論中,“值”In fact is much more complex than that.具體來說,It also captures the information needed to play multiple notes at the same time.例如,When playing music works,You can press the two piano keys at the same time（Play multiple notes at the same time will produce the so-called“和弦”）.But we don't need to talk about music theory too much detail.For the purpose of this assignment,你需要知道的是,We will get the value of the data set,並將學習RNNSequence model to generate the value.

我們的音樂生成系統將使用78個唯一值.Run the following code to load the music data and its pretreatment for value.這可能需要幾分鐘.

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 dimension for ( m , T x , 78 ) (m,T_x,78) (m,Tx​,78)的數組.我們有 m m m個訓練示例,Each training sample is T x = 30 T_x=30 Tx​=30The value of music.在每個時間步,輸入都是78One of the possible values for different,表示為one-hot向量.因此,例如,X[i,t,:]是一個one-hot向量,表示在時間t處第iThe value of the sample.
• Y：本質上與X相同,But the left（過去）Move a step.Similar to the dinosaur homework,We use the previous value to predict the next network interested in,因此,給定 x * 1 * , … , x * t * x^{\langle 1\rangle}, \ldots, x^{\langle t \rangle} x*1*,…,x*t*時,We will sequence model of trying to predict y * t * y^{\langle t \rangle} y*t*,然而,"Y"The data be reordered for ( T y , m , 78 ) (T_y,m,78) (Ty​,m,78)的維度,其中 T y = T x T_y=T_x Ty​=Tx​,To facilitate after input toLSTM.
• n_values：The number of the data set different values.即78.
• indices_values：python字典,映射為0-77The value of music.

1.2 模型概述

This is the model we will use the structure.This with you on a laptop dinosaur models used in the similar,The difference is that you will useKeras實現它.架構如下：

We will be in longer music pieces randomly30A fragment to train model of value.So don't bother to set the first input x * 1 * = 0 ⃗ x^{\langle 1 \rangle} = \vec{0} x*1*=0,Because now most of the code segment can use it to represent the dinosaur names at the beginning of the.Audio started in the period of the middle of the music.We will each set for the same length T x = 30 T_x = 30 Tx​=30,Facilitates the vectorization.

2 建立模型

在這一部分中,You will build a music learning and training model.為此,You will need to build a model,The model adopts the dimensions as ( m , T x , 78 ) (m,T_x,78) (m,Tx​,78)的X和維度為 ( T y , m , 78 ) (T_y,m,78) (Ty​,m,78)的Y.我們將使用具有64維隱藏狀態的LSTM,設置n_a = 64.

n_a = 64

This is to create with multiple input and output ofKeras模型的方法.如果你要構建RNN,即使在測試階段,整個輸入序列 x * 1 * , x * 2 * , … , x * T x * x^{\langle 1 \rangle}, x^{\langle 2 \rangle}, \ldots, x^{\langle T_x \rangle} x*1*,x*2*,…,x*Tx​*Are given in advance.例如,如果輸入是單詞,And the output is the label,則KerasHas a simple built-in functions to build the model.但是,For sequence generation,We do not know in advance when the test x * t * x^{\langle t\rangle} x*t*的所有值;相反,我們使用 x * t * = y * t − 1 * x^{\langle t\rangle} = y^{\langle t-1 \rangle} x*t*=y*t−1*一次生成一個.因此,The code will be even more complex,And you will need to implement their ownforLoop iteration to different time step.

函數djmodel()將使用for循環調用LSTM層 T x T_x Tx​次,並且所有 T x T_x Tx​Copy has the same weight.That should not be to initialize the weights every time, T x T_x Tx​Step should be Shared weight.在KerasThe weight of implementation can be Shared in the network layer of the key steps is：

1. 定義層對象（為此,We will use global variables）.
2. In the spread of input calls when these objects.

We have you need the layer object defined as a global variable.Please run a cell to create them.查看KerasDocument to make sure you know what these layers is：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

現在,reshapor, LSTM_cell 和 densorAre layer object,You can use them to achievedjmodel().In order to spread through the layerKeras張量對象X,使用layer_object(X)（If you need more input,則使用layer_object([X,Y])）.例如,reshapor(X)Will through the above definition ofReshape((1,78))層傳播X.

練習：實現djmodel(),你需要執行2個步驟：

1. 創建一個空列表“輸出”At each time step to saveLSTM單元的輸出.
2. 循環 t ∈ 1 , … , T x t \in 1, \ldots, T_x t∈1,…,Tx​:
   • 從X選擇第"t"個時間步向量.Select dimensions should be(78, ).為此,Please use the following code inKeras中創建自定義Lambda層：

x = Lambda(lambda x: X[:,t,:])(X)

查看KerasDocumentation to understand its role.它正在創建一個"臨時"或"未命名"函數（lambdaFunction is the function）,To extract the appropriateone-hot向量,And the function as aKeras的Layer對象應用於X.

+ 將x重塑為(1,78).你可能會發現`reshapor()`層（在下面定義）很有用.
+ 運行x通過LSTM_cell的一個步驟.Remember to use the previous step hide status$a$和單元狀態$c$初始化`LSTM_cell`.使用以下格式：

a, _, c = LSTM_cell(input_x, initial_state=[Previously hidden state, Before the unit state])

+ 使用"densor"通過dense+softmax層傳播LSTM輸出的激活值.
+ Add predictive value to the"output"列表中

def djmodel(Tx, n_a, n_values):
""" 實現這個模型 參數： Tx -- 語料庫的長度 n_a -- 激活值的數量 n_values -- 音樂數據中唯一數據的數量 返回： model -- Keras模型實體 """
# 定義輸入數據的維度
X = Input((Tx, n_values))
# 定義a0, 初始化隱藏狀態
a0 = Input(shape=(n_a,),name="a0")
c0 = Input(shape=(n_a,),name="c0")
a = a0
c = c0
# 第一步：創建一個空的outputs列表來保存LSTM的所有時間步的輸出.
outputs = []
# 第二步：循環
for t in range(Tx):
## 2.A：從X中選擇第“t”個時間步向量
x = Lambda(lambda x:X[:, t, :])(X)
## 2.B：使用reshapor來對x進行重構為(1, n_values)
x = reshapor(x)
## 2.C：單步傳播
a, _, c = LSTM_cell(x, initial_state=[a, c])
## 2.D：使用densor()應用於LSTM_Cell的隱藏狀態輸出
out = densor(a)
## 2.E：把預測值添加到"outputs"列表中
outputs.append(out)
# 第三步：創建模型實體
model = Model(inputs=[X, a0, c0], outputs=outputs)
return model

Run the following cell to define the model.我們將使用Tx=30, n_a=64（LSTMThe dimensions of the activation）和n_values=78.The unit may take a few seconds to run.

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

WARNING:tensorflow:From d:\vr\virtual_environment\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

現在,You need to compile model for training.我們將使用AdamThe optimizer entropy and 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'])

最後,將LSTM的初始狀態a0和c0初始化為零.

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

Now let's fitting model！Because of the loss function want to each time step a list item format provides“Y”,因此我們需要將“Y”轉換為列表.list(Y)是一個包含30個項的列表,Each list item dimensions are（60,78）.讓我們訓練100個epoch.這將需要幾分鐘.

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

WARNING:tensorflow:From d:\vr\virtual_environment\lib\site-packages\keras\backend\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
Epoch 1/100
60/60 [==============================] - 7s 119ms/step - loss: 125.8104 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0000e+00 - dense_1_accuracy_1: 0.0500 - dense_1_accuracy_2: 0.0333 - dense_1_accuracy_3: 0.0000e+00 - dense_1_accuracy_4: 0.0500 - dense_1_accuracy_5: 0.0500 - dense_1_accuracy_6: 0.0667 - dense_1_accuracy_7: 0.0500 - dense_1_accuracy_8: 0.1167 - dense_1_accuracy_9: 0.1167 - dense_1_accuracy_10: 0.0500 - dense_1_accuracy_11: 0.0500 - dense_1_accuracy_12: 0.0667 - dense_1_accuracy_13: 0.1000 - dense_1_accuracy_14: 0.0500 - dense_1_accuracy_15: 0.0833 - dense_1_accuracy_16: 0.0667 - dense_1_accuracy_17: 0.0000e+00 - dense_1_accuracy_18: 0.0833 - dense_1_accuracy_19: 0.0167 - dense_1_accuracy_20: 0.0500 - dense_1_accuracy_21: 0.0667 - dense_1_accuracy_22: 0.0000e+00 - dense_1_accuracy_23: 0.0667 - dense_1_accuracy_24: 0.0167 - dense_1_accuracy_25: 0.0667 - dense_1_accuracy_26: 0.0167 - dense_1_accuracy_27: 0.0500 - dense_1_accuracy_28: 0.0667 - dense_1_accuracy_29: 0.0000e+00
Epoch 2/100
60/60 [==============================] - 0s 1ms/step - loss: 121.4338 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.1333 - dense_1_accuracy_2: 0.1667 - dense_1_accuracy_3: 0.1333 - dense_1_accuracy_4: 0.1167 - dense_1_accuracy_5: 0.1000 - dense_1_accuracy_6: 0.1000 - dense_1_accuracy_7: 0.0667 - dense_1_accuracy_8: 0.1500 - dense_1_accuracy_9: 0.1000 - dense_1_accuracy_10: 0.0667 - dense_1_accuracy_11: 0.0167 - dense_1_accuracy_12: 0.0833 - dense_1_accuracy_13: 0.0833 - dense_1_accuracy_14: 0.0500 - dense_1_accuracy_15: 0.0833 - dense_1_accuracy_16: 0.1000 - dense_1_accuracy_17: 0.0167 - dense_1_accuracy_18: 0.1000 - dense_1_accuracy_19: 0.0667 - dense_1_accuracy_20: 0.0667 - dense_1_accuracy_21: 0.0500 - dense_1_accuracy_22: 0.0833 - dense_1_accuracy_23: 0.0833 - dense_1_accuracy_24: 0.0167 - dense_1_accuracy_25: 0.1167 - dense_1_accuracy_26: 0.0500 - dense_1_accuracy_27: 0.0667 - dense_1_accuracy_28: 0.0333 - dense_1_accuracy_29: 0.0000e+00
Epoch 3/100
60/60 [==============================] - 0s 1ms/step - loss: 116.7514 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.1333 - dense_1_accuracy_2: 0.1500 - dense_1_accuracy_3: 0.1167 - dense_1_accuracy_4: 0.0833 - dense_1_accuracy_5: 0.0667 - dense_1_accuracy_6: 0.0833 - dense_1_accuracy_7: 0.0667 - dense_1_accuracy_8: 0.1167 - dense_1_accuracy_9: 0.1000 - dense_1_accuracy_10: 0.0667 - dense_1_accuracy_11: 0.0167 - dense_1_accuracy_12: 0.0833 - dense_1_accuracy_13: 0.0833 - dense_1_accuracy_14: 0.0500 - dense_1_accuracy_15: 0.0833 - dense_1_accuracy_16: 0.1000 - dense_1_accuracy_17: 0.0167 - dense_1_accuracy_18: 0.1000 - dense_1_accuracy_19: 0.0667 - dense_1_accuracy_20: 0.0667 - dense_1_accuracy_21: 0.0500 - dense_1_accuracy_22: 0.0833 - dense_1_accuracy_23: 0.0833 - dense_1_accuracy_24: 0.0167 - dense_1_accuracy_25: 0.1167 - dense_1_accuracy_26: 0.0500 - dense_1_accuracy_27: 0.0667 - dense_1_accuracy_28: 0.0333 - dense_1_accuracy_29: 0.0000e+00
Epoch 4/100
60/60 [==============================] - 0s 1ms/step - loss: 112.9043 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0833 - dense_1_accuracy_1: 0.1667 - dense_1_accuracy_2: 0.2000 - dense_1_accuracy_3: 0.1833 - dense_1_accuracy_4: 0.1667 - dense_1_accuracy_5: 0.1167 - dense_1_accuracy_6: 0.1167 - dense_1_accuracy_7: 0.1833 - dense_1_accuracy_8: 0.1167 - dense_1_accuracy_9: 0.1167 - dense_1_accuracy_10: 0.1167 - dense_1_accuracy_11: 0.1000 - dense_1_accuracy_12: 0.0833 - dense_1_accuracy_13: 0.0833 - dense_1_accuracy_14: 0.0667 - dense_1_accuracy_15: 0.1000 - dense_1_accuracy_16: 0.1333 - dense_1_accuracy_17: 0.0833 - dense_1_accuracy_18: 0.0500 - dense_1_accuracy_19: 0.1000 - dense_1_accuracy_20: 0.0833 - dense_1_accuracy_21: 0.0500 - dense_1_accuracy_22: 0.0333 - dense_1_accuracy_23: 0.0167 - dense_1_accuracy_24: 0.1167 - dense_1_accuracy_25: 0.1500 - dense_1_accuracy_26: 0.0500 - dense_1_accuracy_27: 0.1000 - dense_1_accuracy_28: 0.1167 - dense_1_accuracy_29: 0.0000e+00
Epoch 5/100
60/60 [==============================] - 0s 1ms/step - loss: 110.5019 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0667 - dense_1_accuracy_1: 0.1000 - dense_1_accuracy_2: 0.1500 - dense_1_accuracy_3: 0.2167 - dense_1_accuracy_4: 0.2333 - dense_1_accuracy_5: 0.1167 - dense_1_accuracy_6: 0.1000 - dense_1_accuracy_7: 0.2333 - dense_1_accuracy_8: 0.0500 - dense_1_accuracy_9: 0.0333 - dense_1_accuracy_10: 0.1000 - dense_1_accuracy_11: 0.1167 - dense_1_accuracy_12: 0.1167 - dense_1_accuracy_13: 0.1500 - dense_1_accuracy_14: 0.1000 - dense_1_accuracy_15: 0.1000 - dense_1_accuracy_16: 0.1333 - dense_1_accuracy_17: 0.1500 - dense_1_accuracy_18: 0.1000 - dense_1_accuracy_19: 0.0667 - dense_1_accuracy_20: 0.1000 - dense_1_accuracy_21: 0.1000 - dense_1_accuracy_22: 0.0667 - dense_1_accuracy_23: 0.0500 - dense_1_accuracy_24: 0.0833 - dense_1_accuracy_25: 0.1500 - dense_1_accuracy_26: 0.0667 - dense_1_accuracy_27: 0.1500 - dense_1_accuracy_28: 0.1167 - dense_1_accuracy_29: 0.0000e+00
Epoch 6/100
60/60 [==============================] - 0s 1ms/step - loss: 107.8270 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0667 - dense_1_accuracy_1: 0.0667 - dense_1_accuracy_2: 0.1500 - dense_1_accuracy_3: 0.2000 - dense_1_accuracy_4: 0.2167 - dense_1_accuracy_5: 0.1000 - dense_1_accuracy_6: 0.1000 - dense_1_accuracy_7: 0.2167 - dense_1_accuracy_8: 0.0667 - dense_1_accuracy_9: 0.0333 - dense_1_accuracy_10: 0.1000 - dense_1_accuracy_11: 0.1167 - dense_1_accuracy_12: 0.1167 - dense_1_accuracy_13: 0.1333 - dense_1_accuracy_14: 0.1000 - dense_1_accuracy_15: 0.1000 - dense_1_accuracy_16: 0.1000 - dense_1_accuracy_17: 0.1333 - dense_1_accuracy_18: 0.0833 - dense_1_accuracy_19: 0.0667 - dense_1_accuracy_20: 0.0833 - dense_1_accuracy_21: 0.1000 - dense_1_accuracy_22: 0.0500 - dense_1_accuracy_23: 0.1000 - dense_1_accuracy_24: 0.1000 - dense_1_accuracy_25: 0.1667 - dense_1_accuracy_26: 0.1000 - dense_1_accuracy_27: 0.1500 - dense_1_accuracy_28: 0.1667 - dense_1_accuracy_29: 0.0000e+00
Epoch 7/100
60/60 [==============================] - 0s 1ms/step - loss: 104.8190 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0667 - dense_1_accuracy_1: 0.0833 - dense_1_accuracy_2: 0.1667 - dense_1_accuracy_3: 0.2167 - dense_1_accuracy_4: 0.2167 - dense_1_accuracy_5: 0.1333 - dense_1_accuracy_6: 0.1167 - dense_1_accuracy_7: 0.2333 - dense_1_accuracy_8: 0.1000 - dense_1_accuracy_9: 0.0667 - dense_1_accuracy_10: 0.1667 - dense_1_accuracy_11: 0.1167 - dense_1_accuracy_12: 0.1167 - dense_1_accuracy_13: 0.2000 - dense_1_accuracy_14: 0.1167 - dense_1_accuracy_15: 0.1333 - dense_1_accuracy_16: 0.1500 - dense_1_accuracy_17: 0.2000 - dense_1_accuracy_18: 0.1500 - dense_1_accuracy_19: 0.1333 - dense_1_accuracy_20: 0.1333 - dense_1_accuracy_21: 0.1667 - dense_1_accuracy_22: 0.1000 - dense_1_accuracy_23: 0.1500 - dense_1_accuracy_24: 0.1000 - dense_1_accuracy_25: 0.2000 - dense_1_accuracy_26: 0.1333 - dense_1_accuracy_27: 0.1667 - dense_1_accuracy_28: 0.1667 - dense_1_accuracy_29: 0.0000e+00
Epoch 8/100
60/60 [==============================] - 0s 1ms/step - loss: 101.2496 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0667 - dense_1_accuracy_1: 0.0667 - dense_1_accuracy_2: 0.1667 - dense_1_accuracy_3: 0.2167 - dense_1_accuracy_4: 0.2167 - dense_1_accuracy_5: 0.1167 - dense_1_accuracy_6: 0.1333 - dense_1_accuracy_7: 0.2667 - dense_1_accuracy_8: 0.1333 - dense_1_accuracy_9: 0.1167 - dense_1_accuracy_10: 0.1833 - dense_1_accuracy_11: 0.0833 - dense_1_accuracy_12: 0.2833 - dense_1_accuracy_13: 0.2833 - dense_1_accuracy_14: 0.1333 - dense_1_accuracy_15: 0.1667 - dense_1_accuracy_16: 0.2167 - dense_1_accuracy_17: 0.1667 - dense_1_accuracy_18: 0.1667 - dense_1_accuracy_19: 0.1667 - dense_1_accuracy_20: 0.1167 - dense_1_accuracy_21: 0.1000 - dense_1_accuracy_22: 0.1333 - dense_1_accuracy_23: 0.1833 - dense_1_accuracy_24: 0.1167 - dense_1_accuracy_25: 0.1000 - dense_1_accuracy_26: 0.1833 - dense_1_accuracy_27: 0.2500 - dense_1_accuracy_28: 0.1167 - dense_1_accuracy_29: 0.0000e+00
Epoch 9/100
60/60 [==============================] - 0s 1ms/step - loss: 97.0479 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0667 - dense_1_accuracy_1: 0.1000 - dense_1_accuracy_2: 0.1833 - dense_1_accuracy_3: 0.2167 - dense_1_accuracy_4: 0.2167 - dense_1_accuracy_5: 0.1167 - dense_1_accuracy_6: 0.1167 - dense_1_accuracy_7: 0.2500 - dense_1_accuracy_8: 0.1167 - dense_1_accuracy_9: 0.1500 - dense_1_accuracy_10: 0.2000 - dense_1_accuracy_11: 0.1167 - dense_1_accuracy_12: 0.2333 - dense_1_accuracy_13: 0.2667 - dense_1_accuracy_14: 0.2167 - dense_1_accuracy_15: 0.1500 - dense_1_accuracy_16: 0.2333 - dense_1_accuracy_17: 0.1833 - dense_1_accuracy_18: 0.2333 - dense_1_accuracy_19: 0.1833 - dense_1_accuracy_20: 0.1500 - dense_1_accuracy_21: 0.1833 - dense_1_accuracy_22: 0.1667 - dense_1_accuracy_23: 0.2000 - dense_1_accuracy_24: 0.1667 - dense_1_accuracy_25: 0.2500 - dense_1_accuracy_26: 0.1833 - dense_1_accuracy_27: 0.3000 - dense_1_accuracy_28: 0.1167 - dense_1_accuracy_29: 0.0000e+00
Epoch 10/100
60/60 [==============================] - 0s 1ms/step - loss: 93.1729 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0667 - dense_1_accuracy_1: 0.1167 - dense_1_accuracy_2: 0.1667 - dense_1_accuracy_3: 0.2000 - dense_1_accuracy_4: 0.2167 - dense_1_accuracy_5: 0.1500 - dense_1_accuracy_6: 0.1167 - dense_1_accuracy_7: 0.2333 - dense_1_accuracy_8: 0.1667 - dense_1_accuracy_9: 0.1500 - dense_1_accuracy_10: 0.2333 - dense_1_accuracy_11: 0.1500 - dense_1_accuracy_12: 0.2667 - dense_1_accuracy_13: 0.3500 - dense_1_accuracy_14: 0.2500 - dense_1_accuracy_15: 0.1667 - dense_1_accuracy_16: 0.2333 - dense_1_accuracy_17: 0.2000 - dense_1_accuracy_18: 0.2333 - dense_1_accuracy_19: 0.1833 - dense_1_accuracy_20: 0.1500 - dense_1_accuracy_21: 0.2500 - dense_1_accuracy_22: 0.1500 - dense_1_accuracy_23: 0.1833 - dense_1_accuracy_24: 0.1500 - dense_1_accuracy_25: 0.2500 - dense_1_accuracy_26: 0.1833 - dense_1_accuracy_27: 0.2500 - dense_1_accuracy_28: 0.1667 - dense_1_accuracy_29: 0.0000e+00
Epoch 11/100
60/60 [==============================] - 0s 1ms/step - loss: 88.8382 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0667 - dense_1_accuracy_1: 0.1167 - dense_1_accuracy_2: 0.2000 - dense_1_accuracy_3: 0.1667 - dense_1_accuracy_4: 0.2167 - dense_1_accuracy_5: 0.1667 - dense_1_accuracy_6: 0.1500 - dense_1_accuracy_7: 0.2167 - dense_1_accuracy_8: 0.2000 - dense_1_accuracy_9: 0.1667 - dense_1_accuracy_10: 0.2167 - dense_1_accuracy_11: 0.1500 - dense_1_accuracy_12: 0.3167 - dense_1_accuracy_13: 0.3167 - dense_1_accuracy_14: 0.1833 - dense_1_accuracy_15: 0.1833 - dense_1_accuracy_16: 0.3000 - dense_1_accuracy_17: 0.1833 - dense_1_accuracy_18: 0.1667 - dense_1_accuracy_19: 0.2167 - dense_1_accuracy_20: 0.2333 - dense_1_accuracy_21: 0.2333 - dense_1_accuracy_22: 0.1833 - dense_1_accuracy_23: 0.2000 - dense_1_accuracy_24: 0.1333 - dense_1_accuracy_25: 0.2333 - dense_1_accuracy_26: 0.2167 - dense_1_accuracy_27: 0.2333 - dense_1_accuracy_28: 0.1333 - dense_1_accuracy_29: 0.0000e+00
Epoch 12/100
60/60 [==============================] - 0s 1ms/step - loss: 84.4568 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0667 - dense_1_accuracy_1: 0.1167 - dense_1_accuracy_2: 0.2167 - dense_1_accuracy_3: 0.1667 - dense_1_accuracy_4: 0.2167 - dense_1_accuracy_5: 0.1833 - dense_1_accuracy_6: 0.1667 - dense_1_accuracy_7: 0.2833 - dense_1_accuracy_8: 0.2333 - dense_1_accuracy_9: 0.1667 - dense_1_accuracy_10: 0.2667 - dense_1_accuracy_11: 0.1500 - dense_1_accuracy_12: 0.3000 - dense_1_accuracy_13: 0.3500 - dense_1_accuracy_14: 0.2333 - dense_1_accuracy_15: 0.1833 - dense_1_accuracy_16: 0.3000 - dense_1_accuracy_17: 0.1833 - dense_1_accuracy_18: 0.2333 - dense_1_accuracy_19: 0.2167 - dense_1_accuracy_20: 0.2333 - dense_1_accuracy_21: 0.2833 - dense_1_accuracy_22: 0.2667 - dense_1_accuracy_23: 0.1833 - dense_1_accuracy_24: 0.1000 - dense_1_accuracy_25: 0.2667 - dense_1_accuracy_26: 0.2500 - dense_1_accuracy_27: 0.1833 - dense_1_accuracy_28: 0.1833 - dense_1_accuracy_29: 0.0000e+00
Epoch 13/100
60/60 [==============================] - 0s 2ms/step - loss: 80.3870 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.0667 - dense_1_accuracy_1: 0.1500 - dense_1_accuracy_2: 0.2167 - dense_1_accuracy_3: 0.2167 - dense_1_accuracy_4: 0.2333 - dense_1_accuracy_5: 0.1667 - dense_1_accuracy_6: 0.2167 - dense_1_accuracy_7: 0.3167 - dense_1_accuracy_8: 0.2667 - dense_1_accuracy_9: 0.2667 - dense_1_accuracy_10: 0.2833 - dense_1_accuracy_11: 0.2667 - dense_1_accuracy_12: 0.3833 - dense_1_accuracy_13: 0.3833 - dense_1_accuracy_14: 0.2500 - dense_1_accuracy_15: 0.2667 - dense_1_accuracy_16: 0.3333 - dense_1_accuracy_17: 0.2833 - dense_1_accuracy_18: 0.2833 - dense_1_accuracy_19: 0.2667 - dense_1_accuracy_20: 0.2667 - dense_1_accuracy_21: 0.2667 - dense_1_accuracy_22: 0.2833 - dense_1_accuracy_23: 0.2833 - dense_1_accuracy_24: 0.1667 - dense_1_accuracy_25: 0.3333 - dense_1_accuracy_26: 0.1667 - dense_1_accuracy_27: 0.2333 - dense_1_accuracy_28: 0.2333 - dense_1_accuracy_29: 0.0000e+00
Epoch 14/100
60/60 [==============================] - 0s 1ms/step - loss: 76.0954 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.1500 - dense_1_accuracy_2: 0.2167 - dense_1_accuracy_3: 0.2333 - dense_1_accuracy_4: 0.2667 - dense_1_accuracy_5: 0.1500 - dense_1_accuracy_6: 0.2500 - dense_1_accuracy_7: 0.3000 - dense_1_accuracy_8: 0.3333 - dense_1_accuracy_9: 0.3833 - dense_1_accuracy_10: 0.2833 - dense_1_accuracy_11: 0.3000 - dense_1_accuracy_12: 0.4500 - dense_1_accuracy_13: 0.4833 - dense_1_accuracy_14: 0.2833 - dense_1_accuracy_15: 0.3500 - dense_1_accuracy_16: 0.3500 - dense_1_accuracy_17: 0.3000 - dense_1_accuracy_18: 0.3667 - dense_1_accuracy_19: 0.3333 - dense_1_accuracy_20: 0.3000 - dense_1_accuracy_21: 0.3833 - dense_1_accuracy_22: 0.2833 - dense_1_accuracy_23: 0.3667 - dense_1_accuracy_24: 0.2167 - dense_1_accuracy_25: 0.3167 - dense_1_accuracy_26: 0.2833 - dense_1_accuracy_27: 0.3667 - dense_1_accuracy_28: 0.2833 - dense_1_accuracy_29: 0.0000e+00
Epoch 15/100
60/60 [==============================] - 0s 1ms/step - loss: 72.4746 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.1833 - dense_1_accuracy_2: 0.2500 - dense_1_accuracy_3: 0.2333 - dense_1_accuracy_4: 0.2833 - dense_1_accuracy_5: 0.2000 - dense_1_accuracy_6: 0.2500 - dense_1_accuracy_7: 0.3167 - dense_1_accuracy_8: 0.4500 - dense_1_accuracy_9: 0.4500 - dense_1_accuracy_10: 0.3500 - dense_1_accuracy_11: 0.3833 - dense_1_accuracy_12: 0.4000 - dense_1_accuracy_13: 0.4667 - dense_1_accuracy_14: 0.2833 - dense_1_accuracy_15: 0.3667 - dense_1_accuracy_16: 0.3833 - dense_1_accuracy_17: 0.4000 - dense_1_accuracy_18: 0.4000 - dense_1_accuracy_19: 0.3167 - dense_1_accuracy_20: 0.3167 - dense_1_accuracy_21: 0.5000 - dense_1_accuracy_22: 0.3833 - dense_1_accuracy_23: 0.4500 - dense_1_accuracy_24: 0.3333 - dense_1_accuracy_25: 0.3667 - dense_1_accuracy_26: 0.4000 - dense_1_accuracy_27: 0.3833 - dense_1_accuracy_28: 0.3833 - dense_1_accuracy_29: 0.0000e+00
Epoch 16/100
60/60 [==============================] - 0s 1ms/step - loss: 68.5501 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.1833 - dense_1_accuracy_2: 0.2500 - dense_1_accuracy_3: 0.2667 - dense_1_accuracy_4: 0.3000 - dense_1_accuracy_5: 0.3000 - dense_1_accuracy_6: 0.2833 - dense_1_accuracy_7: 0.3333 - dense_1_accuracy_8: 0.4000 - dense_1_accuracy_9: 0.4000 - dense_1_accuracy_10: 0.3667 - dense_1_accuracy_11: 0.4333 - dense_1_accuracy_12: 0.4500 - dense_1_accuracy_13: 0.5667 - dense_1_accuracy_14: 0.2833 - dense_1_accuracy_15: 0.3500 - dense_1_accuracy_16: 0.4167 - dense_1_accuracy_17: 0.4500 - dense_1_accuracy_18: 0.5167 - dense_1_accuracy_19: 0.4333 - dense_1_accuracy_20: 0.3667 - dense_1_accuracy_21: 0.4833 - dense_1_accuracy_22: 0.4167 - dense_1_accuracy_23: 0.3500 - dense_1_accuracy_24: 0.3000 - dense_1_accuracy_25: 0.4333 - dense_1_accuracy_26: 0.3667 - dense_1_accuracy_27: 0.3500 - dense_1_accuracy_28: 0.3333 - dense_1_accuracy_29: 0.0000e+00
Epoch 17/100
60/60 [==============================] - 0s 1ms/step - loss: 65.0331 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.1833 - dense_1_accuracy_2: 0.2833 - dense_1_accuracy_3: 0.2833 - dense_1_accuracy_4: 0.3333 - dense_1_accuracy_5: 0.3333 - dense_1_accuracy_6: 0.3333 - dense_1_accuracy_7: 0.3333 - dense_1_accuracy_8: 0.4833 - dense_1_accuracy_9: 0.4333 - dense_1_accuracy_10: 0.4500 - dense_1_accuracy_11: 0.4333 - dense_1_accuracy_12: 0.4667 - dense_1_accuracy_13: 0.5667 - dense_1_accuracy_14: 0.4000 - dense_1_accuracy_15: 0.4000 - dense_1_accuracy_16: 0.5000 - dense_1_accuracy_17: 0.5000 - dense_1_accuracy_18: 0.4833 - dense_1_accuracy_19: 0.4500 - dense_1_accuracy_20: 0.4333 - dense_1_accuracy_21: 0.6000 - dense_1_accuracy_22: 0.4500 - dense_1_accuracy_23: 0.4333 - dense_1_accuracy_24: 0.3167 - dense_1_accuracy_25: 0.4500 - dense_1_accuracy_26: 0.4500 - dense_1_accuracy_27: 0.4667 - dense_1_accuracy_28: 0.4000 - dense_1_accuracy_29: 0.0000e+00
Epoch 18/100
60/60 [==============================] - 0s 1ms/step - loss: 61.6549 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.2000 - dense_1_accuracy_2: 0.3167 - dense_1_accuracy_3: 0.3000 - dense_1_accuracy_4: 0.3500 - dense_1_accuracy_5: 0.3833 - dense_1_accuracy_6: 0.3833 - dense_1_accuracy_7: 0.4500 - dense_1_accuracy_8: 0.4167 - dense_1_accuracy_9: 0.4500 - dense_1_accuracy_10: 0.4833 - dense_1_accuracy_11: 0.4333 - dense_1_accuracy_12: 0.4333 - dense_1_accuracy_13: 0.6000 - dense_1_accuracy_14: 0.3833 - dense_1_accuracy_15: 0.4667 - dense_1_accuracy_16: 0.4333 - dense_1_accuracy_17: 0.5000 - dense_1_accuracy_18: 0.5500 - dense_1_accuracy_19: 0.4833 - dense_1_accuracy_20: 0.4333 - dense_1_accuracy_21: 0.7167 - dense_1_accuracy_22: 0.5333 - dense_1_accuracy_23: 0.4667 - dense_1_accuracy_24: 0.3667 - dense_1_accuracy_25: 0.5000 - dense_1_accuracy_26: 0.4500 - dense_1_accuracy_27: 0.5333 - dense_1_accuracy_28: 0.5000 - dense_1_accuracy_29: 0.0000e+00
Epoch 19/100
60/60 [==============================] - 0s 1ms/step - loss: 58.4755 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.2000 - dense_1_accuracy_2: 0.3500 - dense_1_accuracy_3: 0.3333 - dense_1_accuracy_4: 0.3333 - dense_1_accuracy_5: 0.4000 - dense_1_accuracy_6: 0.5000 - dense_1_accuracy_7: 0.5167 - dense_1_accuracy_8: 0.5333 - dense_1_accuracy_9: 0.5833 - dense_1_accuracy_10: 0.5000 - dense_1_accuracy_11: 0.5333 - dense_1_accuracy_12: 0.5333 - dense_1_accuracy_13: 0.5833 - dense_1_accuracy_14: 0.4000 - dense_1_accuracy_15: 0.4500 - dense_1_accuracy_16: 0.4667 - dense_1_accuracy_17: 0.5333 - dense_1_accuracy_18: 0.5500 - dense_1_accuracy_19: 0.5333 - dense_1_accuracy_20: 0.5333 - dense_1_accuracy_21: 0.6667 - dense_1_accuracy_22: 0.4667 - dense_1_accuracy_23: 0.5333 - dense_1_accuracy_24: 0.4167 - dense_1_accuracy_25: 0.6333 - dense_1_accuracy_26: 0.5667 - dense_1_accuracy_27: 0.5833 - dense_1_accuracy_28: 0.6500 - dense_1_accuracy_29: 0.0000e+00
Epoch 20/100
60/60 [==============================] - 0s 1ms/step - loss: 55.3904 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.2167 - dense_1_accuracy_2: 0.3500 - dense_1_accuracy_3: 0.3333 - dense_1_accuracy_4: 0.3333 - dense_1_accuracy_5: 0.4833 - dense_1_accuracy_6: 0.5500 - dense_1_accuracy_7: 0.5333 - dense_1_accuracy_8: 0.6667 - dense_1_accuracy_9: 0.6000 - dense_1_accuracy_10: 0.5667 - dense_1_accuracy_11: 0.5500 - dense_1_accuracy_12: 0.6500 - dense_1_accuracy_13: 0.6333 - dense_1_accuracy_14: 0.4833 - dense_1_accuracy_15: 0.4833 - dense_1_accuracy_16: 0.5333 - dense_1_accuracy_17: 0.6333 - dense_1_accuracy_18: 0.5500 - dense_1_accuracy_19: 0.5333 - dense_1_accuracy_20: 0.5667 - dense_1_accuracy_21: 0.6333 - dense_1_accuracy_22: 0.5833 - dense_1_accuracy_23: 0.4333 - dense_1_accuracy_24: 0.4833 - dense_1_accuracy_25: 0.6167 - dense_1_accuracy_26: 0.5333 - dense_1_accuracy_27: 0.6000 - dense_1_accuracy_28: 0.6167 - dense_1_accuracy_29: 0.0000e+00
Epoch 21/100
60/60 [==============================] - 0s 1ms/step - loss: 52.4564 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.2667 - dense_1_accuracy_2: 0.3667 - dense_1_accuracy_3: 0.3000 - dense_1_accuracy_4: 0.3500 - dense_1_accuracy_5: 0.5500 - dense_1_accuracy_6: 0.6167 - dense_1_accuracy_7: 0.5833 - dense_1_accuracy_8: 0.6833 - dense_1_accuracy_9: 0.6833 - dense_1_accuracy_10: 0.6167 - dense_1_accuracy_11: 0.5833 - dense_1_accuracy_12: 0.6833 - dense_1_accuracy_13: 0.6167 - dense_1_accuracy_14: 0.5000 - dense_1_accuracy_15: 0.5000 - dense_1_accuracy_16: 0.5333 - dense_1_accuracy_17: 0.6000 - dense_1_accuracy_18: 0.5667 - dense_1_accuracy_19: 0.5667 - dense_1_accuracy_20: 0.7167 - dense_1_accuracy_21: 0.6833 - dense_1_accuracy_22: 0.6500 - dense_1_accuracy_23: 0.5833 - dense_1_accuracy_24: 0.5500 - dense_1_accuracy_25: 0.7000 - dense_1_accuracy_26: 0.5833 - dense_1_accuracy_27: 0.6667 - dense_1_accuracy_28: 0.7000 - dense_1_accuracy_29: 0.0000e+00
Epoch 22/100
60/60 [==============================] - 0s 1ms/step - loss: 49.6620 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.2833 - dense_1_accuracy_2: 0.3667 - dense_1_accuracy_3: 0.3167 - dense_1_accuracy_4: 0.3667 - dense_1_accuracy_5: 0.6167 - dense_1_accuracy_6: 0.6333 - dense_1_accuracy_7: 0.6000 - dense_1_accuracy_8: 0.7167 - dense_1_accuracy_9: 0.6667 - dense_1_accuracy_10: 0.6167 - dense_1_accuracy_11: 0.6667 - dense_1_accuracy_12: 0.7167 - dense_1_accuracy_13: 0.6500 - dense_1_accuracy_14: 0.5500 - dense_1_accuracy_15: 0.6333 - dense_1_accuracy_16: 0.6500 - dense_1_accuracy_17: 0.6333 - dense_1_accuracy_18: 0.7000 - dense_1_accuracy_19: 0.6667 - dense_1_accuracy_20: 0.6833 - dense_1_accuracy_21: 0.7167 - dense_1_accuracy_22: 0.6500 - dense_1_accuracy_23: 0.6667 - dense_1_accuracy_24: 0.5667 - dense_1_accuracy_25: 0.7167 - dense_1_accuracy_26: 0.6500 - dense_1_accuracy_27: 0.6833 - dense_1_accuracy_28: 0.7333 - dense_1_accuracy_29: 0.0000e+00
Epoch 23/100
60/60 [==============================] - 0s 1ms/step - loss: 46.9174 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3000 - dense_1_accuracy_2: 0.3667 - dense_1_accuracy_3: 0.3000 - dense_1_accuracy_4: 0.3833 - dense_1_accuracy_5: 0.5833 - dense_1_accuracy_6: 0.6500 - dense_1_accuracy_7: 0.6333 - dense_1_accuracy_8: 0.7333 - dense_1_accuracy_9: 0.7333 - dense_1_accuracy_10: 0.6667 - dense_1_accuracy_11: 0.7000 - dense_1_accuracy_12: 0.7667 - dense_1_accuracy_13: 0.6667 - dense_1_accuracy_14: 0.6000 - dense_1_accuracy_15: 0.6833 - dense_1_accuracy_16: 0.6667 - dense_1_accuracy_17: 0.7500 - dense_1_accuracy_18: 0.7167 - dense_1_accuracy_19: 0.6167 - dense_1_accuracy_20: 0.7000 - dense_1_accuracy_21: 0.7333 - dense_1_accuracy_22: 0.6500 - dense_1_accuracy_23: 0.7000 - dense_1_accuracy_24: 0.6000 - dense_1_accuracy_25: 0.7333 - dense_1_accuracy_26: 0.6500 - dense_1_accuracy_27: 0.7667 - dense_1_accuracy_28: 0.7333 - dense_1_accuracy_29: 0.0000e+00
Epoch 24/100
60/60 [==============================] - 0s 1ms/step - loss: 44.3451 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3500 - dense_1_accuracy_2: 0.3500 - dense_1_accuracy_3: 0.3167 - dense_1_accuracy_4: 0.4167 - dense_1_accuracy_5: 0.6500 - dense_1_accuracy_6: 0.7000 - dense_1_accuracy_7: 0.7000 - dense_1_accuracy_8: 0.7833 - dense_1_accuracy_9: 0.7333 - dense_1_accuracy_10: 0.6833 - dense_1_accuracy_11: 0.7333 - dense_1_accuracy_12: 0.7833 - dense_1_accuracy_13: 0.6667 - dense_1_accuracy_14: 0.6833 - dense_1_accuracy_15: 0.7167 - dense_1_accuracy_16: 0.7667 - dense_1_accuracy_17: 0.7667 - dense_1_accuracy_18: 0.7500 - dense_1_accuracy_19: 0.7000 - dense_1_accuracy_20: 0.7833 - dense_1_accuracy_21: 0.8000 - dense_1_accuracy_22: 0.8000 - dense_1_accuracy_23: 0.7333 - dense_1_accuracy_24: 0.6833 - dense_1_accuracy_25: 0.8167 - dense_1_accuracy_26: 0.7167 - dense_1_accuracy_27: 0.8000 - dense_1_accuracy_28: 0.7667 - dense_1_accuracy_29: 0.0000e+00
Epoch 25/100
60/60 [==============================] - 0s 1ms/step - loss: 41.9766 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3000 - dense_1_accuracy_2: 0.4167 - dense_1_accuracy_3: 0.3167 - dense_1_accuracy_4: 0.4833 - dense_1_accuracy_5: 0.6667 - dense_1_accuracy_6: 0.7167 - dense_1_accuracy_7: 0.7333 - dense_1_accuracy_8: 0.7833 - dense_1_accuracy_9: 0.7167 - dense_1_accuracy_10: 0.7000 - dense_1_accuracy_11: 0.7333 - dense_1_accuracy_12: 0.8333 - dense_1_accuracy_13: 0.8667 - dense_1_accuracy_14: 0.7333 - dense_1_accuracy_15: 0.7167 - dense_1_accuracy_16: 0.7667 - dense_1_accuracy_17: 0.8167 - dense_1_accuracy_18: 0.8000 - dense_1_accuracy_19: 0.7500 - dense_1_accuracy_20: 0.8333 - dense_1_accuracy_21: 0.7667 - dense_1_accuracy_22: 0.8000 - dense_1_accuracy_23: 0.7833 - dense_1_accuracy_24: 0.7833 - dense_1_accuracy_25: 0.8167 - dense_1_accuracy_26: 0.6833 - dense_1_accuracy_27: 0.8667 - dense_1_accuracy_28: 0.8333 - dense_1_accuracy_29: 0.0000e+00
Epoch 26/100
60/60 [==============================] - 0s 1ms/step - loss: 39.5593 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3167 - dense_1_accuracy_2: 0.4333 - dense_1_accuracy_3: 0.3833 - dense_1_accuracy_4: 0.5000 - dense_1_accuracy_5: 0.7000 - dense_1_accuracy_6: 0.8500 - dense_1_accuracy_7: 0.7167 - dense_1_accuracy_8: 0.7833 - dense_1_accuracy_9: 0.7833 - dense_1_accuracy_10: 0.7333 - dense_1_accuracy_11: 0.7500 - dense_1_accuracy_12: 0.8667 - dense_1_accuracy_13: 0.9000 - dense_1_accuracy_14: 0.8000 - dense_1_accuracy_15: 0.7833 - dense_1_accuracy_16: 0.8833 - dense_1_accuracy_17: 0.8000 - dense_1_accuracy_18: 0.8500 - dense_1_accuracy_19: 0.8500 - dense_1_accuracy_20: 0.9000 - dense_1_accuracy_21: 0.8333 - dense_1_accuracy_22: 0.8500 - dense_1_accuracy_23: 0.8167 - dense_1_accuracy_24: 0.8167 - dense_1_accuracy_25: 0.8333 - dense_1_accuracy_26: 0.7833 - dense_1_accuracy_27: 0.8833 - dense_1_accuracy_28: 0.8333 - dense_1_accuracy_29: 0.0000e+00
Epoch 27/100
60/60 [==============================] - 0s 1ms/step - loss: 37.3176 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3333 - dense_1_accuracy_2: 0.4833 - dense_1_accuracy_3: 0.4167 - dense_1_accuracy_4: 0.5833 - dense_1_accuracy_5: 0.7500 - dense_1_accuracy_6: 0.8500 - dense_1_accuracy_7: 0.7167 - dense_1_accuracy_8: 0.8000 - dense_1_accuracy_9: 0.8333 - dense_1_accuracy_10: 0.7667 - dense_1_accuracy_11: 0.7833 - dense_1_accuracy_12: 0.9000 - dense_1_accuracy_13: 0.9333 - dense_1_accuracy_14: 0.8667 - dense_1_accuracy_15: 0.8500 - dense_1_accuracy_16: 0.9000 - dense_1_accuracy_17: 0.8833 - dense_1_accuracy_18: 0.8333 - dense_1_accuracy_19: 0.8500 - dense_1_accuracy_20: 0.9167 - dense_1_accuracy_21: 0.9000 - dense_1_accuracy_22: 0.9000 - dense_1_accuracy_23: 0.8333 - dense_1_accuracy_24: 0.8500 - dense_1_accuracy_25: 0.8167 - dense_1_accuracy_26: 0.8000 - dense_1_accuracy_27: 0.9000 - dense_1_accuracy_28: 0.8667 - dense_1_accuracy_29: 0.0000e+00
Epoch 28/100
60/60 [==============================] - 0s 1ms/step - loss: 35.2965 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3333 - dense_1_accuracy_2: 0.5000 - dense_1_accuracy_3: 0.4500 - dense_1_accuracy_4: 0.5833 - dense_1_accuracy_5: 0.7667 - dense_1_accuracy_6: 0.8500 - dense_1_accuracy_7: 0.7167 - dense_1_accuracy_8: 0.8167 - dense_1_accuracy_9: 0.8667 - dense_1_accuracy_10: 0.8000 - dense_1_accuracy_11: 0.8333 - dense_1_accuracy_12: 0.9167 - dense_1_accuracy_13: 0.9500 - dense_1_accuracy_14: 0.9000 - dense_1_accuracy_15: 0.8833 - dense_1_accuracy_16: 0.9500 - dense_1_accuracy_17: 0.9000 - dense_1_accuracy_18: 0.8500 - dense_1_accuracy_19: 0.8667 - dense_1_accuracy_20: 0.9167 - dense_1_accuracy_21: 0.9167 - dense_1_accuracy_22: 0.9167 - dense_1_accuracy_23: 0.8833 - dense_1_accuracy_24: 0.8833 - dense_1_accuracy_25: 0.8500 - dense_1_accuracy_26: 0.8167 - dense_1_accuracy_27: 0.9333 - dense_1_accuracy_28: 0.8833 - dense_1_accuracy_29: 0.0000e+00
Epoch 29/100
60/60 [==============================] - 0s 1ms/step - loss: 33.1478 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3333 - dense_1_accuracy_2: 0.5000 - dense_1_accuracy_3: 0.4500 - dense_1_accuracy_4: 0.6833 - dense_1_accuracy_5: 0.8167 - dense_1_accuracy_6: 0.8667 - dense_1_accuracy_7: 0.7167 - dense_1_accuracy_8: 0.8833 - dense_1_accuracy_9: 0.9167 - dense_1_accuracy_10: 0.9333 - dense_1_accuracy_11: 0.8833 - dense_1_accuracy_12: 0.9500 - dense_1_accuracy_13: 0.9667 - dense_1_accuracy_14: 0.8667 - dense_1_accuracy_15: 0.8833 - dense_1_accuracy_16: 0.9667 - dense_1_accuracy_17: 0.9500 - dense_1_accuracy_18: 0.9167 - dense_1_accuracy_19: 0.9500 - dense_1_accuracy_20: 0.9333 - dense_1_accuracy_21: 0.9667 - dense_1_accuracy_22: 0.9167 - dense_1_accuracy_23: 0.9667 - dense_1_accuracy_24: 0.9833 - dense_1_accuracy_25: 0.9000 - dense_1_accuracy_26: 0.9500 - dense_1_accuracy_27: 0.9500 - dense_1_accuracy_28: 0.9333 - dense_1_accuracy_29: 0.0000e+00
Epoch 30/100
60/60 [==============================] - 0s 1ms/step - loss: 31.2518 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3333 - dense_1_accuracy_2: 0.5167 - dense_1_accuracy_3: 0.5000 - dense_1_accuracy_4: 0.6833 - dense_1_accuracy_5: 0.8833 - dense_1_accuracy_6: 0.9000 - dense_1_accuracy_7: 0.7833 - dense_1_accuracy_8: 0.9000 - dense_1_accuracy_9: 0.9500 - dense_1_accuracy_10: 0.9500 - dense_1_accuracy_11: 0.9167 - dense_1_accuracy_12: 0.9833 - dense_1_accuracy_13: 0.9833 - dense_1_accuracy_14: 0.9500 - dense_1_accuracy_15: 0.9500 - dense_1_accuracy_16: 0.9833 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 0.9667 - dense_1_accuracy_19: 0.9833 - dense_1_accuracy_20: 0.9333 - dense_1_accuracy_21: 0.9667 - dense_1_accuracy_22: 0.9333 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 0.9833 - dense_1_accuracy_25: 0.9500 - dense_1_accuracy_26: 0.9500 - dense_1_accuracy_27: 0.9667 - dense_1_accuracy_28: 0.9500 - dense_1_accuracy_29: 0.0000e+00
Epoch 31/100
60/60 [==============================] - 0s 1ms/step - loss: 29.4504 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3333 - dense_1_accuracy_2: 0.5500 - dense_1_accuracy_3: 0.5500 - dense_1_accuracy_4: 0.7000 - dense_1_accuracy_5: 0.9000 - dense_1_accuracy_6: 0.9167 - dense_1_accuracy_7: 0.8667 - dense_1_accuracy_8: 0.9000 - dense_1_accuracy_9: 0.9333 - dense_1_accuracy_10: 0.9500 - dense_1_accuracy_11: 0.9500 - dense_1_accuracy_12: 0.9833 - dense_1_accuracy_13: 0.9667 - dense_1_accuracy_14: 0.9333 - dense_1_accuracy_15: 0.9667 - dense_1_accuracy_16: 0.9833 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 0.9667 - dense_1_accuracy_19: 0.9833 - dense_1_accuracy_20: 0.9500 - dense_1_accuracy_21: 0.9667 - dense_1_accuracy_22: 0.9833 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 0.9500 - dense_1_accuracy_26: 0.9500 - dense_1_accuracy_27: 0.9667 - dense_1_accuracy_28: 0.9667 - dense_1_accuracy_29: 0.0000e+00
Epoch 32/100
60/60 [==============================] - 0s 1ms/step - loss: 27.6794 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.3667 - dense_1_accuracy_2: 0.5667 - dense_1_accuracy_3: 0.5833 - dense_1_accuracy_4: 0.7333 - dense_1_accuracy_5: 0.9000 - dense_1_accuracy_6: 0.9333 - dense_1_accuracy_7: 0.8833 - dense_1_accuracy_8: 0.9333 - dense_1_accuracy_9: 0.9500 - dense_1_accuracy_10: 0.9500 - dense_1_accuracy_11: 0.9667 - dense_1_accuracy_12: 0.9833 - dense_1_accuracy_13: 0.9833 - dense_1_accuracy_14: 0.9500 - dense_1_accuracy_15: 0.9667 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 0.9833 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 0.9833 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 0.9833 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 0.9833 - dense_1_accuracy_25: 0.9667 - dense_1_accuracy_26: 0.9500 - dense_1_accuracy_27: 0.9667 - dense_1_accuracy_28: 0.9500 - dense_1_accuracy_29: 0.0000e+00
Epoch 33/100
60/60 [==============================] - 0s 1ms/step - loss: 26.0487 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.4000 - dense_1_accuracy_2: 0.6167 - dense_1_accuracy_3: 0.6667 - dense_1_accuracy_4: 0.7833 - dense_1_accuracy_5: 0.9167 - dense_1_accuracy_6: 0.9500 - dense_1_accuracy_7: 0.9000 - dense_1_accuracy_8: 0.9500 - dense_1_accuracy_9: 0.9667 - dense_1_accuracy_10: 0.9833 - dense_1_accuracy_11: 0.9667 - dense_1_accuracy_12: 0.9833 - dense_1_accuracy_13: 0.9833 - dense_1_accuracy_14: 0.9500 - dense_1_accuracy_15: 0.9667 - dense_1_accuracy_16: 0.9833 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 0.9833 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 0.9667 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 0.9833 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 0.9667 - dense_1_accuracy_26: 0.9333 - dense_1_accuracy_27: 0.9667 - dense_1_accuracy_28: 0.9667 - dense_1_accuracy_29: 0.0000e+00
Epoch 34/100
60/60 [==============================] - 0s 1ms/step - loss: 24.5103 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.4000 - dense_1_accuracy_2: 0.6333 - dense_1_accuracy_3: 0.7000 - dense_1_accuracy_4: 0.7833 - dense_1_accuracy_5: 0.9333 - dense_1_accuracy_6: 0.9500 - dense_1_accuracy_7: 0.9167 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 0.9833 - dense_1_accuracy_11: 0.9833 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 0.9833 - dense_1_accuracy_14: 0.9833 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 0.9833 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 0.9833 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 0.9833 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 0.9833 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 0.9833 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9667 - dense_1_accuracy_29: 0.0000e+00
Epoch 35/100
60/60 [==============================] - 0s 1ms/step - loss: 23.1215 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.4333 - dense_1_accuracy_2: 0.7000 - dense_1_accuracy_3: 0.7000 - dense_1_accuracy_4: 0.8500 - dense_1_accuracy_5: 0.9500 - dense_1_accuracy_6: 0.9667 - dense_1_accuracy_7: 0.9333 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 0.9833 - dense_1_accuracy_11: 0.9833 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 0.9833 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 0.9833 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9667 - dense_1_accuracy_29: 0.0000e+00
Epoch 36/100
60/60 [==============================] - 0s 1ms/step - loss: 21.7918 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.4333 - dense_1_accuracy_2: 0.7333 - dense_1_accuracy_3: 0.7167 - dense_1_accuracy_4: 0.8667 - dense_1_accuracy_5: 0.9667 - dense_1_accuracy_6: 0.9500 - dense_1_accuracy_7: 0.9500 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 0.9833 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 0.9833 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9667 - dense_1_accuracy_29: 0.0000e+00
Epoch 37/100
60/60 [==============================] - 0s 1ms/step - loss: 20.5974 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.4167 - dense_1_accuracy_2: 0.7333 - dense_1_accuracy_3: 0.7333 - dense_1_accuracy_4: 0.8833 - dense_1_accuracy_5: 0.9667 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 0.9667 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 0.9833 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 0.9833 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9667 - dense_1_accuracy_29: 0.0000e+00
Epoch 38/100
60/60 [==============================] - 0s 1ms/step - loss: 19.4903 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.4833 - dense_1_accuracy_2: 0.7333 - dense_1_accuracy_3: 0.7333 - dense_1_accuracy_4: 0.8833 - dense_1_accuracy_5: 0.9667 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 0.9833 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 0.9833 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9667 - dense_1_accuracy_29: 0.0000e+00
Epoch 39/100
60/60 [==============================] - 0s 1ms/step - loss: 18.4846 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5000 - dense_1_accuracy_2: 0.7833 - dense_1_accuracy_3: 0.7833 - dense_1_accuracy_4: 0.8833 - dense_1_accuracy_5: 0.9667 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 0.9833 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 0.9833 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9667 - dense_1_accuracy_29: 0.0000e+00
Epoch 40/100
60/60 [==============================] - 0s 1ms/step - loss: 17.5442 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5000 - dense_1_accuracy_2: 0.8000 - dense_1_accuracy_3: 0.8000 - dense_1_accuracy_4: 0.8833 - dense_1_accuracy_5: 0.9833 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 0.9833 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 41/100
60/60 [==============================] - 0s 1ms/step - loss: 16.6850 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5000 - dense_1_accuracy_2: 0.8000 - dense_1_accuracy_3: 0.8000 - dense_1_accuracy_4: 0.9167 - dense_1_accuracy_5: 0.9833 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 0.9833 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 42/100
60/60 [==============================] - 0s 1ms/step - loss: 15.9531 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5000 - dense_1_accuracy_2: 0.8333 - dense_1_accuracy_3: 0.8167 - dense_1_accuracy_4: 0.9333 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 0.9833 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 43/100
60/60 [==============================] - 0s 1ms/step - loss: 15.2606 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5167 - dense_1_accuracy_2: 0.8333 - dense_1_accuracy_3: 0.8500 - dense_1_accuracy_4: 0.9500 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 0.9833 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 44/100
60/60 [==============================] - 0s 1ms/step - loss: 14.6169 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5333 - dense_1_accuracy_2: 0.8333 - dense_1_accuracy_3: 0.8500 - dense_1_accuracy_4: 0.9667 - dense_1_accuracy_5: 0.9833 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 45/100
60/60 [==============================] - 0s 1ms/step - loss: 14.0359 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5333 - dense_1_accuracy_2: 0.8333 - dense_1_accuracy_3: 0.8500 - dense_1_accuracy_4: 0.9667 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 46/100
60/60 [==============================] - 0s 1ms/step - loss: 13.5365 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5333 - dense_1_accuracy_2: 0.8333 - dense_1_accuracy_3: 0.8500 - dense_1_accuracy_4: 0.9667 - dense_1_accuracy_5: 0.9833 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 47/100
60/60 [==============================] - 0s 1ms/step - loss: 13.0739 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5333 - dense_1_accuracy_2: 0.8333 - dense_1_accuracy_3: 0.8500 - dense_1_accuracy_4: 0.9667 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 48/100
60/60 [==============================] - 0s 1ms/step - loss: 12.6324 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5333 - dense_1_accuracy_2: 0.8333 - dense_1_accuracy_3: 0.8667 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 49/100
60/60 [==============================] - 0s 1ms/step - loss: 12.2587 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5500 - dense_1_accuracy_2: 0.8333 - dense_1_accuracy_3: 0.8667 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 50/100
60/60 [==============================] - 0s 1ms/step - loss: 11.9156 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5500 - dense_1_accuracy_2: 0.8500 - dense_1_accuracy_3: 0.8667 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 0.9833 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 51/100
60/60 [==============================] - 0s 1ms/step - loss: 11.6561 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5500 - dense_1_accuracy_2: 0.8667 - dense_1_accuracy_3: 0.9000 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 0.9833 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 52/100
60/60 [==============================] - 0s 1ms/step - loss: 11.3376 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5500 - dense_1_accuracy_2: 0.8667 - dense_1_accuracy_3: 0.9000 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 53/100
60/60 [==============================] - 0s 1ms/step - loss: 11.0278 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5500 - dense_1_accuracy_2: 0.8667 - dense_1_accuracy_3: 0.9000 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 54/100
60/60 [==============================] - 0s 1ms/step - loss: 10.7868 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5500 - dense_1_accuracy_2: 0.8667 - dense_1_accuracy_3: 0.9333 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 55/100
60/60 [==============================] - 0s 1ms/step - loss: 10.6172 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.5500 - dense_1_accuracy_2: 0.8667 - dense_1_accuracy_3: 0.9333 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 0.9833 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 56/100
60/60 [==============================] - 0s 1ms/step - loss: 10.3823 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6000 - dense_1_accuracy_2: 0.8667 - dense_1_accuracy_3: 0.9333 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 57/100
60/60 [==============================] - 0s 1ms/step - loss: 10.1989 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6000 - dense_1_accuracy_2: 0.8667 - dense_1_accuracy_3: 0.9500 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 58/100
60/60 [==============================] - 0s 1ms/step - loss: 10.0857 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6000 - dense_1_accuracy_2: 0.8667 - dense_1_accuracy_3: 0.9500 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 59/100
60/60 [==============================] - 0s 1ms/step - loss: 9.9101 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6000 - dense_1_accuracy_2: 0.8667 - dense_1_accuracy_3: 0.9667 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 60/100
60/60 [==============================] - 0s 1ms/step - loss: 9.7498 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6000 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9667 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 61/100
60/60 [==============================] - 0s 1ms/step - loss: 9.5569 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6000 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9667 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 62/100
60/60 [==============================] - 0s 1ms/step - loss: 9.5195 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6000 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9667 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 63/100
60/60 [==============================] - 0s 1ms/step - loss: 9.3869 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6167 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9667 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 64/100
60/60 [==============================] - 0s 1ms/step - loss: 9.2721 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6167 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9667 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 65/100
60/60 [==============================] - 0s 1ms/step - loss: 9.1552 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6167 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 66/100
60/60 [==============================] - 0s 1ms/step - loss: 9.0480 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6167 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 67/100
60/60 [==============================] - 0s 1ms/step - loss: 8.9713 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6167 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 68/100
60/60 [==============================] - 0s 1ms/step - loss: 8.8916 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6167 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 69/100
60/60 [==============================] - 0s 1ms/step - loss: 8.8817 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6167 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 70/100
60/60 [==============================] - 0s 1ms/step - loss: 8.7221 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6167 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 71/100
60/60 [==============================] - 0s 1ms/step - loss: 8.5989 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 72/100
60/60 [==============================] - 0s 1ms/step - loss: 8.6101 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 73/100
60/60 [==============================] - 0s 1ms/step - loss: 8.4869 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 74/100
60/60 [==============================] - 0s 1ms/step - loss: 8.6417 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.8833 - dense_1_accuracy_3: 0.9833 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 0.9833 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 75/100
60/60 [==============================] - 0s 1ms/step - loss: 8.3961 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9000 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 76/100
60/60 [==============================] - 0s 1ms/step - loss: 8.4834 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9000 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 77/100
60/60 [==============================] - 0s 1ms/step - loss: 8.3441 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9000 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 78/100
60/60 [==============================] - 0s 1ms/step - loss: 8.1681 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9000 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 0.9833 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 79/100
60/60 [==============================] - 0s 1ms/step - loss: 8.2874 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9000 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 80/100
60/60 [==============================] - 0s 1ms/step - loss: 8.1880 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9000 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 81/100
60/60 [==============================] - 0s 1ms/step - loss: 8.1446 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9000 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 82/100
60/60 [==============================] - 0s 1ms/step - loss: 8.0556 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9000 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 83/100
60/60 [==============================] - 0s 1ms/step - loss: 7.9504 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 84/100
60/60 [==============================] - 0s 1ms/step - loss: 7.8864 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 85/100
60/60 [==============================] - 0s 1ms/step - loss: 7.8184 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 86/100
60/60 [==============================] - 0s 1ms/step - loss: 8.2067 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6333 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 0.9667 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 87/100
60/60 [==============================] - 0s 1ms/step - loss: 7.6905 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6500 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 88/100
60/60 [==============================] - 0s 1ms/step - loss: 8.3132 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6500 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 0.9833 - dense_1_accuracy_25: 0.9667 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 0.9833 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 89/100
60/60 [==============================] - 0s 1ms/step - loss: 7.8230 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6500 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 90/100
60/60 [==============================] - 0s 1ms/step - loss: 8.0172 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6500 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 91/100
60/60 [==============================] - 0s 1ms/step - loss: 7.5494 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6500 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 0.9833 - dense_1_accuracy_29: 0.0000e+00
Epoch 92/100
60/60 [==============================] - 0s 1ms/step - loss: 8.0109 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6500 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 93/100
60/60 [==============================] - 0s 1ms/step - loss: 7.7035 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6500 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 94/100
60/60 [==============================] - 0s 1ms/step - loss: 7.4847 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6833 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 95/100
60/60 [==============================] - 0s 1ms/step - loss: 7.7454 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6833 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 96/100
60/60 [==============================] - 0s 1ms/step - loss: 7.4569 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6833 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 97/100
60/60 [==============================] - 0s 1ms/step - loss: 7.4972 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6667 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 98/100
60/60 [==============================] - 0s 1ms/step - loss: 7.6047 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6667 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 99/100
60/60 [==============================] - 0s 1ms/step - loss: 7.3447 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.6833 - dense_1_accuracy_2: 0.9167 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
Epoch 100/100
60/60 [==============================] - 0s 1ms/step - loss: 7.4536 - dense_1_loss: 0.0000e+00 - dense_1_accuracy: 0.1000 - dense_1_accuracy_1: 0.7000 - dense_1_accuracy_2: 0.9333 - dense_1_accuracy_3: 1.0000 - dense_1_accuracy_4: 1.0000 - dense_1_accuracy_5: 1.0000 - dense_1_accuracy_6: 1.0000 - dense_1_accuracy_7: 1.0000 - dense_1_accuracy_8: 1.0000 - dense_1_accuracy_9: 1.0000 - dense_1_accuracy_10: 1.0000 - dense_1_accuracy_11: 1.0000 - dense_1_accuracy_12: 1.0000 - dense_1_accuracy_13: 1.0000 - dense_1_accuracy_14: 1.0000 - dense_1_accuracy_15: 1.0000 - dense_1_accuracy_16: 1.0000 - dense_1_accuracy_17: 1.0000 - dense_1_accuracy_18: 1.0000 - dense_1_accuracy_19: 1.0000 - dense_1_accuracy_20: 1.0000 - dense_1_accuracy_21: 1.0000 - dense_1_accuracy_22: 1.0000 - dense_1_accuracy_23: 1.0000 - dense_1_accuracy_24: 1.0000 - dense_1_accuracy_25: 1.0000 - dense_1_accuracy_26: 1.0000 - dense_1_accuracy_27: 1.0000 - dense_1_accuracy_28: 1.0000 - dense_1_accuracy_29: 0.0000e+00
<keras.callbacks.callbacks.History at 0x2aaedcdfb38>

You can see the loss of model gradually reduce.Now you have trained a model,Let's move on to the last part in order to realize the reasoning algorithm and generate some music！

3 生成音樂

You now have a trained model,This model has learned many solo jazz.Now, let's use this model to synthesize new music.

3.1 Prediction and sampling

In the sampling of each step,你將以LSTMThe activation of previous state“a”和單元狀態“c”作為輸入,Step forward to spread,And get a new output activation and cell state.然後,和之前一樣使用densorThrough the new activationa來生成輸出.

首先,我們將初始化x0以及LSTM激活,And the unit valuea0和c0初始化為零.

You are going to build a function to do this for you infer that.You will use your previous model of function and you want to sampling time step“Ty”.It will return a sequence can be generated for youkeras模型.此外,該函數包含78A unit of dense layer and activation function.

練習：Implement the following function to sample a series of music value.這是在forLoop to generate T y T_y Ty​Output characters need to implement some of the key steps in：

1. 使用LSTM_Cell,Its input step“c”和“a”To generate the current step“c”和“a”.
2. 使用densor（Previously defined）在“a”上計算softmax,In order to get the output of the current step.
3. Add just the generated output to theoutputs中並保存.
4. 將x采樣為“out”的one-hot向量（預測）,In order to pass it on to the nextLSTM步驟.We have provided this line of code,其中使用了Lambda函數.

x = Lambda(one_hot)(out)

[說明：This line of code is, in fact not useoutThe probability to random sampling values,But in each step to useargmaxSelect the most likely a single comment.]

def music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 100):
""" 參數： LSTM_cell -- 來自model()的訓練過後的LSTM單元,是keras層對象. densor -- 來自model()的訓練過後的"densor",是keras層對象 n_values -- 整數,唯一值的數量 n_a -- LSTM單元的數量 Ty -- 整數,生成的是時間步的數量 返回： inference_model -- Kears模型實體 """
# 定義模型輸入的維度
x0 = Input(shape=(1,n_values))
# 定義s0,初始化隱藏狀態
a0 = Input(shape=(n_a,),name="a0")
c0 = Input(shape=(n_a,),name="c0")
a = a0
c = c0
x = x0
# 步驟1：創建一個空的outputs列表來保存預測值.
outputs = []
# 步驟2：遍歷Ty,生成所有時間步的輸出
for t in range(Ty):
# 步驟2.A：在LSTM中單步傳播
a, _, c = LSTM_cell(x, initial_state=[a, c])
# 步驟2.B：使用densor()應用於LSTM_Cell的隱藏狀態輸出
out = densor(a)
# 步驟2.C：預測值添加到"outputs"列表中
outputs.append(out)
# 根據“out”選擇下一個值,並將“x”設置為所選值的一個獨熱編碼,
# 該值將在下一步作為輸入傳遞給LSTM_cell.我們已經提供了執行此操作所需的代碼
x = Lambda(one_hot)(out)
# 創建模型實體
inference_model = Model(inputs=[x0, a0, c0], outputs=outputs)
return inference_model

# 獲取模型實體,模型被硬編碼以產生50個值
inference_model = music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 50)
#創建用於初始化x和LSTM狀態變量a和c的零向量.
x_initializer = np.zeros((1, 1, 78))
a_initializer = np.zeros((1, n_a))
c_initializer = np.zeros((1, n_a))

練習：實現predict_and_sample().This function accepts many parameters,包括輸入[x_initializer, a_initializer, c_initializer].In order to predict corresponding to the input output,You will need to perform3個步驟：

1. According to your input set,Use models to predict the output.輸出pred應該是長度為20的列表,Each of these elements are a shape for ( T y , n _ v a l u e s ) (T_y,n\_values) (Ty​,n_values)的numpy數組.
2. 將pred轉換為 T y T_y Ty​索引的numpy數組.通過使用pred列表中元素的argmaxTo calculate each index.Hint
3. 將索引轉換為one-hot向量表示.Hint

def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer,
c_initializer = c_initializer):
""" Use the model to predict the current value of the next. 參數： inference_model -- keras的實體模型 x_initializer -- 初始化的獨熱編碼,維度為(1, 1, 78) a_initializer -- LSTM單元的隱藏狀態初始化,維度為(1, n_a) c_initializer -- LSTM單元的狀態初始化,維度為(1, n_a) 返回： results -- Generate the value of hot coding vector alone,維度為(Ty, 78) indices -- The generated value index matrix,維度為(Ty, 1) """
# 步驟1：模型來預測給定x_initializer, a_initializer and c_initializer的輸出序列
pred = inference_model.predict([x_initializer, a_initializer, c_initializer])
# 步驟2：將“pred”Converted to has the largest probability index arraynp.array().
indices = np.argmax(pred, axis=-1)
# 步驟3：Converts the index to them a unique thermal code.
results = to_categorical(indices, num_classes=78)
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]) = 62
np.argmax(results[17]) = 30
list(indices[12:18]) = [array([62], dtype=int64), array([30], dtype=int64), array([64], dtype=int64), array([33], dtype=int64), array([62], dtype=int64), array([30], dtype=int64)]

3.3 生成音樂

最後,Are you ready to generate music.你的RNNGenerates a sequence of values.The following code first by calling youpredict_and_sample()Function to generate the music.然後,Will these values post-processing for chord（Means can play multiple values or notes at the same time）.

Most of the calculation of music algorithm USES some post-processing,Because there is no such post processing is difficult to generate sounds nice music.Post processing, such as make sure that the same voice won't repeat too much,Two consecutive notes between pitch are not far apart and so on to deal with each other to generate audio.May someone argue that,There are many in the post-processing steps are hackers.同樣,Generated a lot of music literature also focus on handmade post-processor,And many output quality depends on the quality of post processing,而不僅僅是RNN的質量.But this kind of post-processing is very different,So in our implementation is also try to use it.

Let's start to try to make music！

Run the following cell to generate the music and record it to youout_stream中.這可能需要幾分鐘.

out_stream = generate_music(inference_model)

Predicting new values for different set of chords.
Generated 50 sounds using the predicted values for the set of chords ("1") and after pruning
Generated 50 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 51 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

Want to listen to music,請單擊File->Open…,然後轉到"output/" 並下載 “my_music.midi”.You can use to readMidiFile the application on the computer play the files,Also can use the free online conversion tool"MIDI to mp3"將其轉換為mp3.

作為參考,Here we use this algorithm to generate the30A second audio clips.

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

由於CSDNCan't show the music,Blogger would not be in this show.

This is what you should remember：

• Sequence model can be used to produce music values,Then after processing asMidi音樂.
• You can use a very similar model to generate the dinosaur names or generate music,Is the main difference between model input.
• 在Keras中,Sequence generation includes defining weights of the network layer sharing,And then in different time steps 1 , . . . , T x 1,...,T_x 1,...,Tx​中重復這些步驟.