用LSTM網絡創作一首爵士小歌

在本次作業中，你將使用LSTM實現樂曲生成模型。你可以在作業結束時試聽自己創作的音樂。
你將學習：

• 將LSTM應用於音樂生成。
• 通過深度學習生成自己的爵士樂曲。

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

你向專門為你朋友的生日創作一首爵士樂曲。但是，你不了解任何樂器或音樂作品。幸運的是，你懂得深度學習並且可以使用LSTM網絡來嘗試解決此問題。

你將訓練一個網絡，根據已表演作品的風格生成新穎的爵士小歌。

1.1 數據集

你將在爵士樂曲語料庫上訓練算法。運行下面的單元格以試聽訓練集中的音頻片段：

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

由於CSDN無法展示音樂，博主就不在此展示了。

我們已經對音樂數據進行了預處理，以根據音樂“value”呈現音樂數據。你可以將每個“值”視為一個音符，其中包括一個音調和一個持續時間。例如，如果你按下特定的鋼琴鍵0.5秒鐘，則你剛剛演奏了一個音符。在音樂理論中，“值”實際上比這復雜得多。具體來說，它還捕獲同時演奏多個音符所需的信息。例如，演奏音樂作品時，你可以同時按下兩個鋼琴鍵（同時演奏多個音符會產生所謂的“和弦”）。但是我們不需要討論音樂理論的過多細節。出於此作業的目的，你需要知道的是，我們將獲取值的數據集，並將學習RNN模型以生成值序列。

我們的音樂生成系統將使用78個唯一值。運行以下代碼以加載原始音樂數據並將其預處理為值。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)

你剛剛加載了以下內容：

• X：這是維度為 ( 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，每個訓練示例都是 T x = 30 T_x=30 Tx​=30音樂值的摘要。在每個時間步，輸入都是78個不同的可能值之一，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i個示例的值。
• Y：本質上與X相同，但是向左（過去）移了一步。與恐龍作業相似，我們對使用先前值預測下一個值的網絡感興趣，因此，給定 x * 1 * , … , x * t * x^{\langle 1\rangle}, \ldots, x^{\langle t \rangle} x*1*,…,x*t*時，我們的序列模型將嘗試預測 y * t * y^{\langle t \rangle} y*t*，然而，"Y"中的數據被重新排序為 ( 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​，以方便之後輸入到LSTM。
• n_values：該數據集中不同值的數量。即78。
• indices_values：python字典，映射為0-77的音樂值。

1.2 模型概述

這是我們將使用的模型結構。這與你在上一個筆記本中使用的恐龍模型相似，不同之處在於你將用Keras實現它。架構如下：

我們將在更長的音樂片段中隨機抽取30個值的片段來訓練模型。因此不必費心設置第一個輸入 x * 1 * = 0 ⃗ x^{\langle 1 \rangle} = \vec{0} x*1*=0，因為現在大部分代碼段都用它來表示恐龍名稱的開頭。音頻開始於一段音樂的中間。我們將每個片段設置為相同的長度 T x = 30 T_x = 30 Tx​=30，使得向量化更加容易。

2 建立模型

在這一部分中，你將構建和訓練一個音樂學習模型。為此，你將需要構建一個模型，該模型采用維度為 ( 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

這是創建具有多個輸入和輸出的Keras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​*都是預先給定。例如，如果輸入是單詞，而輸出是標簽，則Keras具有簡單的內置函數來構建模型。但是，對於序列生成，在測試時我們並不預先知道 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，代碼將更加復雜，並且你將需要實現自己的for循環來迭代不同的時間步。

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​副本都具有相同的權重。即不應該每次都重新初始化權重， T x T_x Tx​步應該具有共享的權重。在Keras中實現可共享權重的網絡層的關鍵步驟是：

1. 定義層對象（為此，我們將使用全局變量）。
2. 在傳播輸入時調用這些對象。

我們已經將你需要的層對象定義為全局變量。請運行下一個單元格以創建它們。查看Keras文檔以確保你了解這些層是什麼：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 densor都是層對象，你可以使用它們來實現djmodel()。為了通過這些層傳播Keras張量對象X，使用layer_object(X)（如果需要多個輸入，則使用layer_object([X,Y])）。例如，reshapor(X)將通過上面定義的Reshape((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”在每個時間步保存的LSTM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。選擇的維度應為(78, )。為此，請使用以下代碼行在Keras中創建自定義Lambda層：

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

查看Keras文檔以了解其作用。它正在創建一個"臨時"或"未命名"函數（lambda函數就是該函數），以提取適當的one-hot向量，並將該函數作為Keras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。記住要使用上一步的隱藏狀態$a$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=[以前的隱藏狀態, 以前的單元狀態])

+ 使用"densor"通過dense+softmax層傳播LSTM輸出的激活值。
+ 將預測值添加到"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

運行以下單元格以定義模型。我們將使用Tx=30, n_a=64（LSTM激活的維數）和n_values=78。該單元可能需要幾秒鐘才能運行。

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，你需要編譯模型以進行訓練。我們將使用Adam優化器和交叉熵熵損失。

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))

現在讓我們擬合模型！由於損失函數希望以每個時間步一個列表項的格式提供“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，其中每個列表項的維度均為（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>

你可以看到模型的損失逐漸減少。現在你已經訓練好了一個模型，讓我們繼續最後一部分以實現推理算法並生成一些樂曲！

3 生成音樂

你現在擁有一個訓練好的模型，該模型已經學習了許多爵士獨奏。現在讓我們使用此模型來合成新音樂。

3.1 預測和采樣

在采樣的每個步驟中，你將以LSTM先前狀態的激活“a”和單元狀態“c”作為輸入，向前傳播一步，並獲得新的輸出激活以及單元狀態。然後，和之前一樣使用densor通過新的激活a來生成輸出。

首先，我們將初始化x0以及LSTM激活，並將單元值a0和c0初始化為零。

你將要構建一個函數來為你進行此推斷。你的函數將采用你先前的模型以及你要采樣的時間步長“Ty”。它將返回一個可以為你生成序列的keras模型。此外，該函數包含78個單位的密集層和激活函數。

練習：實現以下函數以采樣一系列音樂值。這是在for循環內生成 T y T_y Ty​輸出字符需要實現的一些關鍵步驟：

1. 使用LSTM_Cell，它輸入上一步的“c”和“a”來生成當前步驟的“c”和“a”。
2. 使用densor（先前定義）在“a”上計算softmax，以獲取當前步驟的輸出。
3. 將剛剛生成的輸出添加到outputs中並保存。
4. 將x采樣為“out”的one-hot向量（預測），以便將其傳遞到下一個LSTM步驟。我們已經提供了這行代碼，其中使用了Lambda函數。

x = Lambda(one_hot)(out)

[說明：這行代碼實際上不是使用out中的概率對值進行隨機采樣，而是在每個步驟中使用argmax選擇最可能的單個注釋。]

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()。此函數接受許多參數，包括輸入[x_initializer, a_initializer, c_initializer]。為了預測與此輸入相對應的輸出，你將需要執行3個步驟：

1. 根據你的輸入集，使用模型預測輸出。輸出pred應該是長度為20的列表，其中每個元素都是一個形狀為 ( 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argmax來計算每個對應的索引。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):
""" 使用模型預測當前值的下一個值。 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 -- 生成值的獨熱編碼向量，維度為(Ty, 78) indices -- 所生成值的索引矩陣，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”轉換為具有最大概率的索引數組np.array()。
indices = np.argmax(pred, axis=-1)
# 步驟3：將索引轉換為它們的一個獨熱編碼。
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，你准備好生成音樂了。你的RNN會生成一個值序列。以下代碼首先通過調用你的predict_and_sample()函數來生成音樂。然後，將這些值後期處理為和弦（意味著可以同時演奏多個值或音符）。

大多數計算音樂算法都使用某些後期處理，因為沒有這種後期處理很難生成聽起來不錯的音樂。後期處理通過諸如確保相同的聲音不會重復太多，兩個連續的音符彼此之間的音高相距不遠等來處理生成的音頻。可能有人爭辯說，這些後期處理步驟中有很多都是黑客。同樣，很多音樂生成文學也集中於手工制作後處理器，並且許多輸出質量取決於後期處理的質量，而不僅僅是RNN的質量。但是這種後期處理的確有很大的不同，因此在我們的實現中也試著使用它。

讓我們開始嘗試制作音樂吧！

運行以下單元格來生成音樂並將其記錄到你的out_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

要試聽音樂，請單擊File->Open…，然後轉到"output/" 並下載 “my_music.midi”。你可以使用可讀取Midi文件的應用程序在計算機上播放該文件，也可以使用免費在線轉換工具"MIDI to mp3"將其轉換為mp3。

作為參考，下面我們使用此算法生成的30秒音頻剪輯。

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

由於CSDN無法展示音樂，博主就不在此展示了。

這是你應該記住的：

• 序列模型可用於生成音樂值，然後將其後處理為Midi音樂。
• 可以使用非常相似的模型來生成恐龍名稱或生成音樂，主要區別是模型的輸入。
• 在Keras中，序列生成包括定義具有共享權重的網絡層，然後在不同的時間步 1 , . . . , T x 1,...,T_x 1,...,Tx​中重復這些步驟。