Python深度学习之搭建小型卷积神经网络（Kaggle网站Dogs-vs-Cats数据集）

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我是靠谱客的博主甜美抽屉，这篇文章主要介绍Python深度学习之搭建小型卷积神经网络（Kaggle网站Dogs-vs-Cats数据集），现在分享给大家，希望可以做个参考。

完全来源与《Python深度学习》中的例子，仅供学习只用。

Cats vs. Dogs（猫狗大战）是Kaggle大数据竞赛的数据集，数据集由训练数据和测试数据组成，训练数据包含猫和狗各12500张图片，测试数据包含12500张猫和狗的图片。

我们取Cats vs. Dogs数据集中的2000个测试，1000验证，1000个测试，其中猫狗各一半。。

在2000个训练样本上训练一个简单的小型卷积神经网络，不做任何正则化，为模型目标设定一个基准，这会得到73%的分类精度。

示例源码如下：

复制代码

#!/usr/bin/env python3

import os, shutil
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator

original_dataset_dir = r'D:Kaggle Datasetsdogs-vs-catstrain'
base_dir = r'D:Pycharm ProjectsDogs-vs-Catscats_and_dogs_images'
if not os.path.exists(base_dir):
    os.mkdir(base_dir)

train_dir = os.path.join(base_dir, 'train')
if not os.path.exists(train_dir):
    os.mkdir(train_dir)

validation_dir = os.path.join(base_dir, 'validation')
if not os.path.exists(validation_dir):
    os.mkdir(validation_dir)

test_dir = os.path.join(base_dir, 'test')
if not os.path.exists(test_dir):
    os.mkdir(test_dir)

train_dogs_dir = os.path.join(train_dir, 'dogs')
if not os.path.exists(train_dogs_dir):
    os.mkdir(train_dogs_dir)

train_cats_dir = os.path.join(train_dir, 'cats')
if not os.path.exists(train_cats_dir):
    os.mkdir(train_cats_dir)

validation_dogs_dir = os.path.join(validation_dir, 'dogs')
if not os.path.exists(validation_dogs_dir):
    os.mkdir(validation_dogs_dir)

validation_cats_dir = os.path.join(validation_dir, 'cats')
if not os.path.exists(validation_cats_dir):
    os.mkdir(validation_cats_dir)

test_dogs_dir = os.path.join(test_dir, 'dogs')
if not os.path.exists(test_dogs_dir):
    os.mkdir(test_dogs_dir)

test_cats_dir = os.path.join(test_dir, 'cats')
if not os.path.exists(test_cats_dir):
    os.mkdir(test_cats_dir)

fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(train_dogs_dir, fname)
    shutil.copy(src, dst)

fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(validation_dogs_dir, fname)
    shutil.copy(src, dst)

fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(test_dogs_dir, fname)
    shutil.copy(src, dst)

fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(train_cats_dir, fname)
    shutil.copy(src, dst)

fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(validation_cats_dir, fname)
    shutil.copy(src, dst)

fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(test_cats_dir, fname)
    shutil.copy(src, dst)

print('total training dog images:', len(os.listdir(train_dogs_dir)))
print('total training cat images:', len(os.listdir(train_cats_dir)))
print('total validation dog images:', len(os.listdir(validation_dogs_dir)))
print('total validation cat images:', len(os.listdir(validation_cats_dir)))
print('total test dog images:', len(os.listdir(test_dogs_dir)))
print('total test cat images:', len(os.listdir(test_cats_dir)))

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.summary()

model.compile(loss='binary_crossentropy',
              optimizer=optimizers.RMSprop(lr=1e-4),
              metrics=['acc'])

train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(train_dir,
                                                    target_size=(150, 150),
                                                    batch_size=20,
                                                    class_mode='binary')
validation_generator = validation_datagen.flow_from_directory(validation_dir,
                                                        target_size=(150, 150),
                                                        batch_size=20,
                                                        class_mode='binary')
test_generator = test_datagen.flow_from_directory(test_dir,
                                                  target_size=(150, 150),
                                                  batch_size=20,
                                                  class_mode='binary')

model.fit_generator(train_generator,
                    steps_per_epoch=100,
                    epochs=20,
                    validation_data=validation_generator,
                    validation_steps=50)

model.save('cats_and_dogs_small.h5')

model = models.load_model('cats_and_dogs_small.h5')

model.evaluate_generator(test_generator, workers=5, use_multiprocessing=False, verbose=1)

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#!/usr/bin/env python3

import os, shutil
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator


original_dataset_dir = r'D:Kaggle Datasetsdogs-vs-catstrain'
base_dir = r'D:Pycharm ProjectsDogs-vs-Catscats_and_dogs_images'
if not os.path.exists(base_dir):
    os.mkdir(base_dir)

train_dir = os.path.join(base_dir, 'train')
if not os.path.exists(train_dir):
    os.mkdir(train_dir)

validation_dir = os.path.join(base_dir, 'validation')
if not os.path.exists(validation_dir):
    os.mkdir(validation_dir)

test_dir = os.path.join(base_dir, 'test')
if not os.path.exists(test_dir):
    os.mkdir(test_dir)

train_dogs_dir = os.path.join(train_dir, 'dogs')
if not os.path.exists(train_dogs_dir):
    os.mkdir(train_dogs_dir)

train_cats_dir = os.path.join(train_dir, 'cats')
if not os.path.exists(train_cats_dir):
    os.mkdir(train_cats_dir)

validation_dogs_dir = os.path.join(validation_dir, 'dogs')
if not os.path.exists(validation_dogs_dir):
    os.mkdir(validation_dogs_dir)

validation_cats_dir = os.path.join(validation_dir, 'cats')
if not os.path.exists(validation_cats_dir):
    os.mkdir(validation_cats_dir)

test_dogs_dir = os.path.join(test_dir, 'dogs')
if not os.path.exists(test_dogs_dir):
    os.mkdir(test_dogs_dir)

test_cats_dir = os.path.join(test_dir, 'cats')
if not os.path.exists(test_cats_dir):
    os.mkdir(test_cats_dir)

fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(train_dogs_dir, fname)
    shutil.copy(src, dst)

fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(validation_dogs_dir, fname)
    shutil.copy(src, dst)

fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(test_dogs_dir, fname)
    shutil.copy(src, dst)

fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(train_cats_dir, fname)
    shutil.copy(src, dst)

fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(validation_cats_dir, fname)
    shutil.copy(src, dst)

fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(test_cats_dir, fname)
    shutil.copy(src, dst)


print('total training dog images:', len(os.listdir(train_dogs_dir)))
print('total training cat images:', len(os.listdir(train_cats_dir)))
print('total validation dog images:', len(os.listdir(validation_dogs_dir)))
print('total validation cat images:', len(os.listdir(validation_cats_dir)))
print('total test dog images:', len(os.listdir(test_dogs_dir)))
print('total test cat images:', len(os.listdir(test_cats_dir)))


model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.summary()

model.compile(loss='binary_crossentropy',
              optimizer=optimizers.RMSprop(lr=1e-4),
              metrics=['acc'])

train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(train_dir,
                                                    target_size=(150, 150),
                                                    batch_size=20,
                                                    class_mode='binary')
validation_generator = validation_datagen.flow_from_directory(validation_dir,
                                                        target_size=(150, 150),
                                                        batch_size=20,
                                                        class_mode='binary')
test_generator = test_datagen.flow_from_directory(test_dir,
                                                  target_size=(150, 150),
                                                  batch_size=20,
                                                  class_mode='binary')

model.fit_generator(train_generator,
                    steps_per_epoch=100,
                    epochs=20,
                    validation_data=validation_generator,
                    validation_steps=50)

model.save('cats_and_dogs_small.h5')

model = models.load_model('cats_and_dogs_small.h5')

model.evaluate_generator(test_generator, workers=5, use_multiprocessing=False, verbose=1)

运行结果如下：

复制代码

"C:Program FilesPythonPython37python.exe" "D:/Pycharm Projects/Dogs-vs-Cats/Dogs-vs-Cats.py"
total training dog images: 1000
total training cat images: 1000
total validation dog images: 500
total validation cat images: 500
total test dog images: 500
total test cat images: 500
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 148, 148, 32)      896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 74, 74, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 72, 72, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 34, 34, 128)       73856     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128)       0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 15, 15, 128)       147584    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 6272)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               3211776   
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 513       
=================================================================
Total params: 3,453,121
Trainable params: 3,453,121
Non-trainable params: 0
_________________________________________________________________
Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Epoch 1/20
WARNING:tensorflow:From C:Program FilesPythonPython37libsite-packagestensorflow_corepythonopsmath_grad.py:1394: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

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100/100 [==============================] - 79s 794ms/step - loss: 0.6903 - acc: 0.5290 - val_loss: 0.6729 - val_acc: 0.6020
Epoch 2/20

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100/100 [==============================] - 78s 782ms/step - loss: 0.6563 - acc: 0.6030 - val_loss: 0.6369 - val_acc: 0.6350
Epoch 3/20

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100/100 [==============================] - 78s 782ms/step - loss: 0.6108 - acc: 0.6680 - val_loss: 0.6073 - val_acc: 0.6780
Epoch 4/20

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100/100 [==============================] - 80s 798ms/step - loss: 0.5636 - acc: 0.7150 - val_loss: 0.5932 - val_acc: 0.6930
Epoch 5/20

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100/100 [==============================] - 79s 793ms/step - loss: 0.5457 - acc: 0.7230 - val_loss: 0.5998 - val_acc: 0.6770
Epoch 6/20

1/100 [..............................] - ETA: 1:12 - loss: 0.5432 - acc: 0.8000
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100/100 [==============================] - 79s 789ms/step - loss: 0.5099 - acc: 0.7375 - val_loss: 0.5761 - val_acc: 0.7030
Epoch 7/20

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100/100 [==============================] - 78s 784ms/step - loss: 0.4872 - acc: 0.7590 - val_loss: 0.6340 - val_acc: 0.6600
Epoch 8/20

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100/100 [==============================] - 78s 784ms/step - loss: 0.4627 - acc: 0.7790 - val_loss: 0.5452 - val_acc: 0.7240
Epoch 9/20

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100/100 [==============================] - 79s 786ms/step - loss: 0.4440 - acc: 0.7895 - val_loss: 0.5512 - val_acc: 0.7240
Epoch 10/20

1/100 [..............................] - ETA: 1:08 - loss: 0.4543 - acc: 0.8000
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100/100 [==============================] - 81s 810ms/step - loss: 0.4191 - acc: 0.8010 - val_loss: 0.5972 - val_acc: 0.6850
Epoch 11/20

1/100 [..............................] - ETA: 1:08 - loss: 0.5956 - acc: 0.7000
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100/100 [==============================] - 78s 783ms/step - loss: 0.3964 - acc: 0.8290 - val_loss: 0.5416 - val_acc: 0.7350
Epoch 12/20

1/100 [..............................] - ETA: 1:07 - loss: 0.3022 - acc: 0.8500
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100/100 [==============================] - 78s 785ms/step - loss: 0.3673 - acc: 0.8400 - val_loss: 0.5783 - val_acc: 0.7210
Epoch 13/20

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100/100 [==============================] - 78s 784ms/step - loss: 0.3581 - acc: 0.8385 - val_loss: 0.6111 - val_acc: 0.7210
Epoch 14/20

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100/100 [==============================] - 79s 786ms/step - loss: 0.3366 - acc: 0.8580 - val_loss: 0.5784 - val_acc: 0.7260
Epoch 15/20

1/100 [..............................] - ETA: 1:07 - loss: 0.2318 - acc: 0.9000
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100/100 [==============================] - 78s 785ms/step - loss: 0.3064 - acc: 0.8750 - val_loss: 0.5539 - val_acc: 0.7270
Epoch 16/20

1/100 [..............................] - ETA: 1:10 - loss: 0.2998 - acc: 0.9000
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100/100 [==============================] - 79s 787ms/step - loss: 0.2950 - acc: 0.8735 - val_loss: 0.5851 - val_acc: 0.7410
Epoch 17/20

1/100 [..............................] - ETA: 1:07 - loss: 0.2987 - acc: 0.9000
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100/100 [==============================] - 78s 785ms/step - loss: 0.2736 - acc: 0.8875 - val_loss: 0.5749 - val_acc: 0.7450
Epoch 18/20

1/100 [..............................] - ETA: 1:07 - loss: 0.1544 - acc: 1.0000
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100/100 [==============================] - 78s 784ms/step - loss: 0.2462 - acc: 0.9010 - val_loss: 0.5997 - val_acc: 0.7360
Epoch 19/20

1/100 [..............................] - ETA: 1:07 - loss: 0.1225 - acc: 0.9500
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100/100 [==============================] - 79s 786ms/step - loss: 0.2309 - acc: 0.9120 - val_loss: 0.6209 - val_acc: 0.7320
Epoch 20/20

1/100 [..............................] - ETA: 1:07 - loss: 0.0996 - acc: 1.0000
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100/100 [==============================] - 79s 785ms/step - loss: 0.2102 - acc: 0.9220 - val_loss: 0.6511 - val_acc: 0.7280

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Process finished with exit code 0

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"C:Program FilesPythonPython37python.exe" "D:/Pycharm Projects/Dogs-vs-Cats/Dogs-vs-Cats.py"
total training dog images: 1000
total training cat images: 1000
total validation dog images: 500
total validation cat images: 500
total test dog images: 500
total test cat images: 500
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 148, 148, 32)      896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 74, 74, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 72, 72, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 34, 34, 128)       73856     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128)       0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 15, 15, 128)       147584    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 6272)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               3211776   
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 513       
=================================================================
Total params: 3,453,121
Trainable params: 3,453,121
Non-trainable params: 0
_________________________________________________________________
Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Epoch 1/20
WARNING:tensorflow:From C:Program FilesPythonPython37libsite-packagestensorflow_corepythonopsmath_grad.py:1394: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

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100/100 [==============================] - 79s 794ms/step - loss: 0.6903 - acc: 0.5290 - val_loss: 0.6729 - val_acc: 0.6020
Epoch 2/20

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100/100 [==============================] - 78s 782ms/step - loss: 0.6563 - acc: 0.6030 - val_loss: 0.6369 - val_acc: 0.6350
Epoch 3/20

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100/100 [==============================] - 78s 782ms/step - loss: 0.6108 - acc: 0.6680 - val_loss: 0.6073 - val_acc: 0.6780
Epoch 4/20

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100/100 [==============================] - 80s 798ms/step - loss: 0.5636 - acc: 0.7150 - val_loss: 0.5932 - val_acc: 0.6930
Epoch 5/20

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100/100 [==============================] - 79s 793ms/step - loss: 0.5457 - acc: 0.7230 - val_loss: 0.5998 - val_acc: 0.6770
Epoch 6/20

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100/100 [==============================] - 79s 789ms/step - loss: 0.5099 - acc: 0.7375 - val_loss: 0.5761 - val_acc: 0.7030
Epoch 7/20

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100/100 [==============================] - 78s 784ms/step - loss: 0.4872 - acc: 0.7590 - val_loss: 0.6340 - val_acc: 0.6600
Epoch 8/20

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100/100 [==============================] - 78s 784ms/step - loss: 0.4627 - acc: 0.7790 - val_loss: 0.5452 - val_acc: 0.7240
Epoch 9/20

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100/100 [==============================] - 79s 786ms/step - loss: 0.4440 - acc: 0.7895 - val_loss: 0.5512 - val_acc: 0.7240
Epoch 10/20

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100/100 [==============================] - 81s 810ms/step - loss: 0.4191 - acc: 0.8010 - val_loss: 0.5972 - val_acc: 0.6850
Epoch 11/20

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100/100 [==============================] - 78s 783ms/step - loss: 0.3964 - acc: 0.8290 - val_loss: 0.5416 - val_acc: 0.7350
Epoch 12/20

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100/100 [==============================] - 78s 785ms/step - loss: 0.3673 - acc: 0.8400 - val_loss: 0.5783 - val_acc: 0.7210
Epoch 13/20

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100/100 [==============================] - 78s 784ms/step - loss: 0.3581 - acc: 0.8385 - val_loss: 0.6111 - val_acc: 0.7210
Epoch 14/20

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100/100 [==============================] - 79s 786ms/step - loss: 0.3366 - acc: 0.8580 - val_loss: 0.5784 - val_acc: 0.7260
Epoch 15/20

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100/100 [==============================] - 78s 785ms/step - loss: 0.3064 - acc: 0.8750 - val_loss: 0.5539 - val_acc: 0.7270
Epoch 16/20

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100/100 [==============================] - 79s 787ms/step - loss: 0.2950 - acc: 0.8735 - val_loss: 0.5851 - val_acc: 0.7410
Epoch 17/20

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100/100 [==============================] - 78s 785ms/step - loss: 0.2736 - acc: 0.8875 - val_loss: 0.5749 - val_acc: 0.7450
Epoch 18/20

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100/100 [==============================] - 78s 784ms/step - loss: 0.2462 - acc: 0.9010 - val_loss: 0.5997 - val_acc: 0.7360
Epoch 19/20

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100/100 [==============================] - 79s 786ms/step - loss: 0.2309 - acc: 0.9120 - val_loss: 0.6209 - val_acc: 0.7320
Epoch 20/20

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100/100 [==============================] - 79s 785ms/step - loss: 0.2102 - acc: 0.9220 - val_loss: 0.6511 - val_acc: 0.7280

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