我是靠谱客的博主 甜美抽屉，最近开发中收集的这篇文章主要介绍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），觉得挺不错的，现在分享给大家，希望可以做个参考。
概述
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：
#!/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：
"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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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
1/100 [..............................] - ETA: 1:07 - loss: 0.6600 - acc: 0.5500
2/100 [..............................] - ETA: 1:06 - loss: 0.6788 - acc: 0.5000
3/100 [..............................] - ETA: 1:07 - loss: 0.6832 - acc: 0.5500
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97/100 [============================>.] - ETA: 2s - loss: 0.6115 - acc: 0.6634
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99/100 [============================>.] - ETA: 0s - loss: 0.6116 - acc: 0.6657
100/100 [==============================] - 78s 782ms/step - loss: 0.6108 - acc: 0.6680 - val_loss: 0.6073 - val_acc: 0.6780
Epoch 4/20
1/100 [..............................] - ETA: 1:07 - loss: 0.5794 - acc: 0.7500
2/100 [..............................] - ETA: 1:06 - loss: 0.5491 - acc: 0.7500
3/100 [..............................] - ETA: 1:05 - loss: 0.5312 - acc: 0.7667
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99/100 [============================>.] - ETA: 0s - loss: 0.5644 - acc: 0.7141
100/100 [==============================] - 80s 798ms/step - loss: 0.5636 - acc: 0.7150 - val_loss: 0.5932 - val_acc: 0.6930
Epoch 5/20
1/100 [..............................] - ETA: 1:06 - loss: 0.4588 - acc: 0.8000
2/100 [..............................] - ETA: 1:05 - loss: 0.6144 - acc: 0.6250
3/100 [..............................] - ETA: 1:05 - loss: 0.6550 - acc: 0.6167
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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
1/100 [..............................] - ETA: 1:08 - loss: 0.6860 - acc: 0.6000
2/100 [..............................] - ETA: 1:08 - loss: 0.7118 - acc: 0.6250
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99/100 [============================>.] - ETA: 0s - loss: 0.4829 - acc: 0.7621
100/100 [==============================] - 78s 784ms/step - loss: 0.4872 - acc: 0.7590 - val_loss: 0.6340 - val_acc: 0.6600
Epoch 8/20
1/100 [..............................] - ETA: 1:07 - loss: 0.5683 - acc: 0.6500
2/100 [..............................] - ETA: 1:06 - loss: 0.4909 - acc: 0.7750
3/100 [..............................] - ETA: 1:05 - loss: 0.4766 - acc: 0.7833
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6/100 [>.............................] - ETA: 1:04 - loss: 0.4354 - acc: 0.7917
7/100 [=>............................] - ETA: 1:03 - loss: 0.4401 - acc: 0.7786
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10/100 [==>...........................] - ETA: 1:01 - loss: 0.4466 - acc: 0.7650
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86/100 [========================>.....] - ETA: 9s - loss: 0.4685 - acc: 0.7744
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94/100 [===========================>..] - ETA: 4s - loss: 0.4633 - acc: 0.7798
95/100 [===========================>..] - ETA: 3s - loss: 0.4632 - acc: 0.7784
96/100 [===========================>..] - ETA: 2s - loss: 0.4627 - acc: 0.7786
97/100 [============================>.] - ETA: 2s - loss: 0.4632 - acc: 0.7784
98/100 [============================>.] - ETA: 1s - loss: 0.4626 - acc: 0.7786
99/100 [============================>.] - ETA: 0s - loss: 0.4637 - acc: 0.7778
100/100 [==============================] - 78s 784ms/step - loss: 0.4627 - acc: 0.7790 - val_loss: 0.5452 - val_acc: 0.7240
Epoch 9/20
1/100 [..............................] - ETA: 1:14 - loss: 0.3899 - acc: 0.8500
2/100 [..............................] - ETA: 1:10 - loss: 0.3776 - acc: 0.8750
3/100 [..............................] - ETA: 1:08 - loss: 0.3394 - acc: 0.9000
4/100 [>.............................] - ETA: 1:07 - loss: 0.3648 - acc: 0.8750
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9/100 [=>............................] - ETA: 1:03 - loss: 0.3927 - acc: 0.8444
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97/100 [============================>.] - ETA: 2s - loss: 0.4415 - acc: 0.7928
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99/100 [============================>.] - ETA: 0s - loss: 0.4429 - acc: 0.7909
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
2/100 [..............................] - ETA: 1:06 - loss: 0.4659 - acc: 0.7250
3/100 [..............................] - ETA: 1:07 - loss: 0.4139 - acc: 0.7833
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6/100 [>.............................] - ETA: 1:04 - loss: 0.3847 - acc: 0.8000
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10/100 [==>...........................] - ETA: 1:01 - loss: 0.4028 - acc: 0.8000
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86/100 [========================>.....] - ETA: 9s - loss: 0.4173 - acc: 0.7994
87/100 [=========================>....] - ETA: 9s - loss: 0.4163 - acc: 0.8000
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97/100 [============================>.] - ETA: 2s - loss: 0.4209 - acc: 0.8005
98/100 [============================>.] - ETA: 1s - loss: 0.4194 - acc: 0.8015
99/100 [============================>.] - ETA: 0s - loss: 0.4196 - acc: 0.8010
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
2/100 [..............................] - ETA: 1:09 - loss: 0.5289 - acc: 0.7750
3/100 [..............................] - ETA: 1:07 - loss: 0.4657 - acc: 0.8167
4/100 [>.............................] - ETA: 1:06 - loss: 0.4595 - acc: 0.8250
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9/100 [=>............................] - ETA: 1:02 - loss: 0.4037 - acc: 0.8333
10/100 [==>...........................] - ETA: 1:01 - loss: 0.3796 - acc: 0.8450
11/100 [==>...........................] - ETA: 1:01 - loss: 0.3758 - acc: 0.8500
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13/100 [==>...........................] - ETA: 59s - loss: 0.3720 - acc: 0.8538
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86/100 [========================>.....] - ETA: 9s - loss: 0.3916 - acc: 0.8302
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94/100 [===========================>..] - ETA: 4s - loss: 0.3970 - acc: 0.8271
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96/100 [===========================>..] - ETA: 2s - loss: 0.3967 - acc: 0.8276
97/100 [============================>.] - ETA: 2s - loss: 0.3968 - acc: 0.8284
98/100 [============================>.] - ETA: 1s - loss: 0.3967 - acc: 0.8291
99/100 [============================>.] - ETA: 0s - loss: 0.3975 - acc: 0.8278
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
2/100 [..............................] - ETA: 1:06 - loss: 0.2612 - acc: 0.9000
3/100 [..............................] - ETA: 1:05 - loss: 0.2900 - acc: 0.8667
4/100 [>.............................] - ETA: 1:06 - loss: 0.3213 - acc: 0.8750
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12/100 [==>...........................] - ETA: 1:00 - loss: 0.3647 - acc: 0.8625
13/100 [==>...........................] - ETA: 59s - loss: 0.3685 - acc: 0.8615
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94/100 [===========================>..] - ETA: 4s - loss: 0.3650 - acc: 0.8426
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97/100 [============================>.] - ETA: 2s - loss: 0.3666 - acc: 0.8402
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99/100 [============================>.] - ETA: 0s - loss: 0.3681 - acc: 0.8394
100/100 [==============================] - 78s 785ms/step - loss: 0.3673 - acc: 0.8400 - val_loss: 0.5783 - val_acc: 0.7210
Epoch 13/20
1/100 [..............................] - ETA: 1:11 - loss: 0.3179 - acc: 0.8500
2/100 [..............................] - ETA: 1:08 - loss: 0.3822 - acc: 0.8000
3/100 [..............................] - ETA: 1:07 - loss: 0.4198 - acc: 0.7833
4/100 [>.............................] - ETA: 1:06 - loss: 0.3727 - acc: 0.8125
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10/100 [==>...........................] - ETA: 1:01 - loss: 0.3458 - acc: 0.8450
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13/100 [==>...........................] - ETA: 59s - loss: 0.3432 - acc: 0.8500
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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
1/100 [..............................] - ETA: 1:08 - loss: 0.3348 - acc: 0.8500
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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
2/100 [..............................] - ETA: 1:06 - loss: 0.2582 - acc: 0.9000
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97/100 [============================>.] - ETA: 2s - loss: 0.3056 - acc: 0.8758
98/100 [============================>.] - ETA: 1s - loss: 0.3060 - acc: 0.8755
99/100 [============================>.] - ETA: 0s - loss: 0.3058 - acc: 0.8753
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
2/100 [..............................] - ETA: 1:08 - loss: 0.3017 - acc: 0.9250
3/100 [..............................] - ETA: 1:07 - loss: 0.3415 - acc: 0.9167
4/100 [>.............................] - ETA: 1:06 - loss: 0.3747 - acc: 0.8625
5/100 [>.............................] - ETA: 1:05 - loss: 0.3828 - acc: 0.8600
6/100 [>.............................] - ETA: 1:04 - loss: 0.3713 - acc: 0.8583
7/100 [=>............................] - ETA: 1:03 - loss: 0.3592 - acc: 0.8571
8/100 [=>............................] - ETA: 1:03 - loss: 0.3420 - acc: 0.8687
9/100 [=>............................] - ETA: 1:02 - loss: 0.3266 - acc: 0.8778
10/100 [==>...........................] - ETA: 1:01 - loss: 0.3179 - acc: 0.8800
11/100 [==>...........................] - ETA: 1:00 - loss: 0.3210 - acc: 0.8727
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13/100 [==>...........................] - ETA: 59s - loss: 0.3094 - acc: 0.8885
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17/100 [====>.........................] - ETA: 56s - loss: 0.2827 - acc: 0.9000
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86/100 [========================>.....] - ETA: 9s - loss: 0.2909 - acc: 0.8721
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91/100 [==========================>...] - ETA: 6s - loss: 0.2941 - acc: 0.8703
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94/100 [===========================>..] - ETA: 4s - loss: 0.2961 - acc: 0.8707
95/100 [===========================>..] - ETA: 3s - loss: 0.2978 - acc: 0.8700
96/100 [===========================>..] - ETA: 2s - loss: 0.2975 - acc: 0.8703
97/100 [============================>.] - ETA: 2s - loss: 0.2957 - acc: 0.8716
98/100 [============================>.] - ETA: 1s - loss: 0.2951 - acc: 0.8730
99/100 [============================>.] - ETA: 0s - loss: 0.2953 - acc: 0.8732
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
2/100 [..............................] - ETA: 1:06 - loss: 0.3061 - acc: 0.9000
3/100 [..............................] - ETA: 1:06 - loss: 0.3075 - acc: 0.8500
4/100 [>.............................] - ETA: 1:06 - loss: 0.2877 - acc: 0.8750
5/100 [>.............................] - ETA: 1:05 - loss: 0.2585 - acc: 0.9000
6/100 [>.............................] - ETA: 1:04 - loss: 0.2839 - acc: 0.8917
7/100 [=>............................] - ETA: 1:03 - loss: 0.2631 - acc: 0.9000
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9/100 [=>............................] - ETA: 1:02 - loss: 0.2819 - acc: 0.8944
10/100 [==>...........................] - ETA: 1:01 - loss: 0.2755 - acc: 0.8950
11/100 [==>...........................] - ETA: 1:00 - loss: 0.2679 - acc: 0.8955
12/100 [==>...........................] - ETA: 1:00 - loss: 0.2534 - acc: 0.9042
13/100 [==>...........................] - ETA: 59s - loss: 0.2594 - acc: 0.8962
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94/100 [===========================>..] - ETA: 4s - loss: 0.2756 - acc: 0.8867
95/100 [===========================>..] - ETA: 3s - loss: 0.2743 - acc: 0.8874
96/100 [===========================>..] - ETA: 2s - loss: 0.2735 - acc: 0.8880
97/100 [============================>.] - ETA: 2s - loss: 0.2730 - acc: 0.8881
98/100 [============================>.] - ETA: 1s - loss: 0.2729 - acc: 0.8883
99/100 [============================>.] - ETA: 0s - loss: 0.2732 - acc: 0.8874
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
2/100 [..............................] - ETA: 1:06 - loss: 0.1380 - acc: 1.0000
3/100 [..............................] - ETA: 1:05 - loss: 0.1465 - acc: 0.9833
4/100 [>.............................] - ETA: 1:05 - loss: 0.1622 - acc: 0.9750
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6/100 [>.............................] - ETA: 1:04 - loss: 0.1967 - acc: 0.9333
7/100 [=>............................] - ETA: 1:03 - loss: 0.1934 - acc: 0.9286
8/100 [=>............................] - ETA: 1:02 - loss: 0.1966 - acc: 0.9187
9/100 [=>............................] - ETA: 1:01 - loss: 0.2171 - acc: 0.9056
10/100 [==>...........................] - ETA: 1:01 - loss: 0.2151 - acc: 0.9100
11/100 [==>...........................] - ETA: 1:00 - loss: 0.2158 - acc: 0.9091
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13/100 [==>...........................] - ETA: 59s - loss: 0.2086 - acc: 0.9192
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41/100 [===========>..................] - ETA: 40s - loss: 0.2443 - acc: 0.9000
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97/100 [============================>.] - ETA: 2s - loss: 0.2477 - acc: 0.9010
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99/100 [============================>.] - ETA: 0s - loss: 0.2471 - acc: 0.9005
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
2/100 [..............................] - ETA: 1:08 - loss: 0.1306 - acc: 0.9500
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7/100 [=>............................] - ETA: 1:03 - loss: 0.1828 - acc: 0.9357
8/100 [=>............................] - ETA: 1:03 - loss: 0.1780 - acc: 0.9438
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86/100 [========================>.....] - ETA: 9s - loss: 0.2350 - acc: 0.9105
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91/100 [==========================>...] - ETA: 6s - loss: 0.2317 - acc: 0.9126
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94/100 [===========================>..] - ETA: 4s - loss: 0.2297 - acc: 0.9138
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96/100 [===========================>..] - ETA: 2s - loss: 0.2297 - acc: 0.9125
97/100 [============================>.] - ETA: 2s - loss: 0.2301 - acc: 0.9119
98/100 [============================>.] - ETA: 1s - loss: 0.2293 - acc: 0.9122
99/100 [============================>.] - ETA: 0s - loss: 0.2295 - acc: 0.9126
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
2/100 [..............................] - ETA: 1:06 - loss: 0.1583 - acc: 0.9750
3/100 [..............................] - ETA: 1:05 - loss: 0.1902 - acc: 0.9667
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8/100 [=>............................] - ETA: 1:02 - loss: 0.2000 - acc: 0.9375
9/100 [=>............................] - ETA: 1:02 - loss: 0.1896 - acc: 0.9444
10/100 [==>...........................] - ETA: 1:01 - loss: 0.1867 - acc: 0.9400
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13/100 [==>...........................] - ETA: 59s - loss: 0.1722 - acc: 0.9462
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94/100 [===========================>..] - ETA: 4s - loss: 0.2100 - acc: 0.9229
95/100 [===========================>..] - ETA: 3s - loss: 0.2086 - acc: 0.9237
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97/100 [============================>.] - ETA: 2s - loss: 0.2086 - acc: 0.9237
98/100 [============================>.] - ETA: 1s - loss: 0.2089 - acc: 0.9230
99/100 [============================>.] - ETA: 0s - loss: 0.2090 - acc: 0.9227
100/100 [==============================] - 79s 785ms/step - loss: 0.2102 - acc: 0.9220 - val_loss: 0.6511 - val_acc: 0.7280
1/50 [..............................] - ETA: 15s - loss: 0.4642 - acc: 0.8500
2/50 [>.............................] - ETA: 12s - loss: 0.3972 - acc: 0.8250
3/50 [>.............................] - ETA: 11s - loss: 0.5752 - acc: 0.8000
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最后
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