深度学习⼤作业：使⽤预训练的神经⽹络预测⼈民币⾯额 from keras.applications import VGG16
Using TensorFlow backend.
conv_base = VGG16(weights='imagenet',
include_top = False)
conv_base.summary()
Model: "vgg16"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, None, None, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, None, None, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, None, None, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, None, None, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, None, None, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, None, None, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, None, None, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, None, None, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, None, None, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, None, None, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, None, None, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, None, None, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, None, None, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, None, None, 512) 0
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________
2.使⽤预训练的卷积基提取特征
import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
base_dir = 'RMB'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
datagen = ImageDataGenerator(rescale=1./255)
batch_size = 20
def extract_features(directory, sample_count):
features = np.zeros(shape=(sample_count, 8, 8, 512))
labels = np.zeros(shape=(sample_count))
generator = datagen.flow_from_directory(
directory,
# target_size=(150, 150),
batch_size=batch_size,
class_mode='binary')
i = 0
for inputs_batch, labels_batch in generator:
features_batch = conv_base.predict(inputs_batch)
features[i * batch_size : (i + 1) * batch_size] = features_batch
labels[i * batch_size : (i + 1) * batch_size] = labels_batch
i += 1
if i * batch_size >= sample_count:
# Note that since generators yield data indefinitely in a loop,
# we must `break` after every image has been seen once.
break
return features, labels
train_features, train_labels = extract_features(train_dir, 40)
validation_features, validation_labels = extract_features(validation_dir, 20)
test_features, test_labels = extract_features(test_dir, 20)
Found 40 images belonging to 2 classes.
Found 20 images belonging to 2 classes.
Found 20 images belonging to 2 classes.
train_features = np.reshape(train_features, (40, 8 * 8 * 512))
validation_features = np.reshape(validation_features, (20, 8 * 8 * 512))
test_features = np.reshape(test_features, (20, 8 * 8 * 512))
from keras import models
from keras import layers
from keras import optimizers
model = models.Sequential()
model.add(layers.Dense(256, activation='relu', input_dim=8 * 8 * 512))
model.add(layers.Dropout(0.3))
# model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
modelpile(optimizer=optimizers.RMSprop(lr=2e-5),
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(train_features, train_labels,
epochs=30,
batch_size=10,
validation_data=(validation_features, validation_labels))
WARNING:tensorflow:From D:\programming_software_install\Anaconda3\lib\site-packages\tensorflow_core\python\ops\nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions f
or updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Train on 40 samples, validate on 20 samples
Epoch 1/30
40/40 [==============================] - 1s 22ms/step - loss: 0.9066 - acc: 0.4500 - val_loss: 0.6638 - val_acc: 0.6000
Epoch 2/30
40/40 [==============================] - 1s 18ms/step - loss: 0.6172 - acc: 0.7000 - val_loss: 0.6057 - val_acc: 0.7000
Epoch 3/30
40/40 [==============================] - 1s 21ms/step - loss: 0.4520 - acc: 0.8500 - val_loss: 0.5790 - val_acc: 0.7500
Epoch 4/30
40/40 [==============================] - 1s 22ms/step - loss: 0.3832 - acc: 0.9000 - val_loss: 0.5674 - val_acc: 0.7500
Epoch 5/30
40/40 [==============================] - 1s 20ms/step - loss: 0.3637 - acc: 0.9000 - val_loss: 0.5548 - val_acc: 0.7000
Epoch 6/30
40/40 [==============================] - 1s 20ms/step - loss: 0.3440 - acc: 0.8500 - val_loss: 0.5190 - val_acc: 0.8500
Epoch 7/30
40/40 [==============================] - 1s 22ms/step - loss: 0.3206 - acc: 0.9250 - val_loss: 0.4981 - val_acc: 0.8000
Epoch 8/30
40/40 [==============================] - 1s 20ms/step - loss: 0.1964 - acc: 1.0000 - val_loss: 0.5424 - val_acc: 0.7500
Epoch 9/30
40/40 [==============================] - 1s 24ms/step - loss: 0.2126 - acc: 1.0000 - val_loss: 0.4715 - val_acc: 0.8000
Epoch 10/30
40/40 [==============================] - 1s 25ms/step - loss: 0.2098 - acc: 1.0000 - val_loss: 0.4685 - val_acc: 0.8000
Epoch 11/30
40/40 [==============================] - 1s 25ms/step - loss: 0.1666 - acc: 1.0000 - val_loss: 0.4792 - val_acc: 0.7500
Epoch 12/30
40/40 [==============================] - 1s 20ms/step - loss: 0.1459 - acc: 1.0000 - val_loss: 0.4475 - val_acc: 0.8000
Epoch 13/30
40/40 [==============================] - 1s 19ms/step - loss: 0.1326 - acc: 1.0000 - val_loss: 0.4358 - val_acc: 0.7500
Epoch 14/30
40/40 [==============================] - 1s 19ms/step - loss: 0.1277 - acc: 0.9750 - val_loss: 0.4512 - val_acc: 0.8000
Epoch 15/30
40/40 [==============================] - 1s 19ms/step - loss: 0.1208 - acc: 1.0000 - val_loss: 0.4153 - val_acc: 0.7500
神经网络预测
Epoch 16/30
40/40 [==============================] - 1s 18ms/step - loss: 0.0925 - acc: 1.0000 - val_loss: 0.4174 - val_acc: 0.8000
Epoch 17/30
40/40 [==============================] - 1s 19ms/step - loss: 0.0792 - acc: 1.0000 - val_loss: 0.4537 - val_acc: 0.7500
Epoch 18/30
40/40 [==============================] - 1s 19ms/step - loss: 0.0611 - acc: 1.0000 - val_loss: 0.3885 - val_acc: 0.8000
Epoch 19/30
40/40 [==============================] - 1s 22ms/step - loss: 0.0494 - acc: 1.0000 - val_loss: 0.4019 - val_acc: 0.8000
Epoch 20/30
40/40 [==============================] - 1s 22ms/step - loss: 0.0496 - acc: 1.0000 - val_loss: 0.4023 - val_acc: 0.8500
Epoch 21/30
40/40 [==============================] - 1s 20ms/step - loss: 0.0446 - acc: 1.0000 - val_loss: 0.3759 - val_acc: 0.8000
Epoch 22/30
40/40 [==============================] - 1s 21ms/step - loss: 0.0383 - acc: 1.0000 - val_loss: 0.4161 - val_acc: 0.8500
Epoch 23/30
40/40 [==============================] - 1s 18ms/step - loss: 0.0375 - acc: 1.0000 - val_loss: 0.3651 - val_acc: 0.8000
Epoch 24/30
40/40 [==============================] - 1s 18ms/step - loss: 0.0320 - acc: 1.0000 - val_loss: 0.3602 - val_acc: 0.8000
Epoch 25/30
40/40 [==============================] - 1s 21ms/step - loss: 0.0379 - acc: 1.0000 - val_loss: 0.3646 - val_acc: 0.8000
Epoch 26/30
40/40 [==============================] - 1s 20ms/step - loss: 0.0361 - acc: 1.0000 - val_loss: 0.3797 - val_acc: 0.8500
Epoch 27/30
40/40 [==============================] - 1s 18ms/step - loss: 0.0197 - acc: 1.0000 - val_loss: 0.3452 - val_acc: 0.8000
Epoch 28/30
40/40 [==============================] - 1s 18ms/step - loss: 0.0154 - acc: 1.0000 - val_loss: 0.3681 - val_acc: 0.8500
Epoch 29/30
40/40 [==============================] - 1s 18ms/step - loss: 0.0198 - acc: 1.0000 - val_loss: 0.3391 - val_acc: 0.8000
Epoch 30/30
40/40 [==============================] - 1s 22ms/step - loss: 0.0201 - acc: 1.0000 - val_loss: 0.4376 - val_acc: 0.8000
4.作图分析
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
不使⽤数据增强的模型过拟合很快，验证准确率在80%左右 数据增强
5.在卷积基上添加有个密集连接分类器
conv_base = VGG16(weights='imagenet',
include_top = False,
input_shape=(150,300,3))
from keras import models
from keras import layers
model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
print('This is the number of trainable weights '
'before freezing the conv base:', ainable_weights))
This is the number of trainable weights before freezing the conv base: 30
# 冻结卷积基
ainable = False
layers.Flatten
print('This is the number of trainable weights '
'after freezing the conv base:', ainable_weights))
This is the number of trainable weights after freezing the conv base: 4
6.利⽤冻结的卷积基端到端的训练模型
from keras.preprocessing.image import ImageDataGenerator
import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras import models
from keras import layers
from keras import optimizers
base_dir = 'RMB'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
# All images will be resized to 150x150
target_size=(150, 300),
batch_size=10,
# Since we use binary_crossentropy loss, we need binary labels
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 300),
batch_size=10,
class_mode='binary')
modelpile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=2e-5),
metrics=['acc'])
history = model.fit_generator(
train_generator,
steps_per_epoch=4, # 40/10=4
epochs=30,
validation_data=validation_generator,
validation_steps=2) # 20/10=2
Found 40 images belonging to 2 classes.
Found 20 images belonging to 2 classes.
Epoch 1/30
4/4 [==============================] - 10s 3s/step - loss: 0.4983 - acc: 0.7250 - val_loss: 0.5309 - val_acc: 0.8500 Epoch 2/30
4/4 [==============================] - 9s 2s/step - loss: 0.3863 - acc: 0.9500 - val_loss: 0.4963 - val_acc: 0.8500 Epoch 3/30
4/4 [==============================] - 9s 2s/step - loss: 0.4130 - acc: 0.9000 - val_loss: 0.3735 - val_acc: 0.8500 Epoch 4/30
4/4 [==============================] - 9s 2s/step - loss: 0.4684 - acc: 0.8000 - val_loss: 0.4199 - val_acc: 0.8500 Epoch 5/30
4/4 [==============================] - 9s 2s/step - loss: 0.4285 - acc: 0.8250 - val_loss: 0.3742 - val_acc: 0.8500 Epoch 6/30
4/4 [==============================] - 9s 2s/step - loss: 0.4281 - acc: 0.8000 - val_loss: 0.4188 - val_acc: 0.8500 Epoch 7/30
4/4 [==============================] - 9s 2s/step - loss: 0.3745 - acc: 0.9500 - val_loss: 0.3599 - val_acc: 0.8500 Epoch 8/30
4/4 [==============================] - 9s 2s/step - loss: 0.3713 - acc: 0.9250 - val_loss: 0.4884 - val_acc: 0.8500 Epoch 9/30
4/4 [==============================] - 9s 2s/step - loss: 0.4812 - acc: 0.7500 - val_loss: 0.3460 - val_acc: 0.8500 Epoch 10/30
4/4 [==============================] - 9s 2s/step - loss: 0.3836 - acc: 0.9250 - val_loss: 0.5165 - val_acc: 0.8000 Epoch 11/30
4/4 [==============================] - 9s 2s/step - loss: 0.4876 - acc: 0.7500 - val_loss: 0.4878 - val_acc: 0.9000 Epoch 12/30
4/4 [==============================] - 9s 2s/step - loss: 0.4094 - acc: 0.9250 - val_loss: 0.5558 - val_acc: 0.9000 Epoch 13/30
4/4 [==============================] - 9s 2s/step - loss: 0.3929 - acc: 0.9250 - val_loss: 0.3510 - val_acc: 0.8500 Epoch 14/30
4/4 [==============================] - 9s 2s/step - loss: 0.4267 - acc: 0.8000 - val_loss: 0.3783 - val_acc: 0.8500
4/4 [==============================] - 9s 2s/step - loss: 0.4903 - acc: 0.7250 - val_loss: 0.4892 - val_acc: 0.8500 Epoch 16/30
4/4 [==============================] - 9s 2s/step - loss: 0.3959 - acc: 0.8750 - val_loss: 0.5783 - val_acc: 0.8500 Epoch 17/30
4/4 [==============================] - 9s 2s/step - loss: 0.3667 - acc: 0.9250 - val_loss: 0.2927 - val_acc: 0.8500 Epoch 18/30
4/4 [==============================] - 9s 2s/step - loss: 0.3357 - acc: 0.9250 - val_loss: 0.3319 - val_acc: 0.8500 Epoch 19/30
4/4 [==============================] - 9s 2s/step - loss: 0.3802 - acc: 0.8750 - val_loss: 0.3748 - val_acc: 0.9000 Epoch 20/30
4/4 [==============================] - 9s 2s/step - loss: 0.3698 - acc: 0.8500 - val_loss: 0.3591 - val_acc: 0.8500 Epoch 21/30
4/4 [==============================] - 9s 2s/step - loss: 0.3461 - acc: 0.8500 - val_loss: 0.5043 - val_acc: 0.8000 Epoch 22/30
4/4 [==============================] - 10s 2s/step - loss: 0.3758 - acc: 0.8750 - val_loss: 0.3844 - val_acc: 0.8500 Epoch 23/30
4/4 [==============================] - 9s 2s/step - loss: 0.3390 - acc: 0.9000 - val_loss: 0.4559 - val_acc: 0.9000 Epoch 24/30
4/4 [==============================] - 9s 2s/step - loss: 0.3153 - acc: 0.9000 - val_loss: 0.4981 - val_acc: 0.8500 Epoch 25/30
4/4 [==============================] - 9s 2s/step - loss: 0.3246 - acc: 0.9500 - val_loss: 0.3254 - val_acc: 0.8000 Epoch 26/30
4/4 [==============================] - 9s 2s/step - loss: 0.3695 - acc: 0.9250 - val_loss: 0.4693 - val_acc: 0.9000 Epoch 27/30
4/4 [==============================] - 9s 2s/step - loss: 0.2785 - acc: 0.9500 - val_loss: 0.3909 - val_acc: 0.7500 Epoch 28/30
4/4 [==============================] - 9s 2s/step - loss: 0.4136 - acc: 0.7750 - val_loss: 0.3878 - val_acc: 0.8500 Epoch 29/30
4/4 [==============================] - 9s 2s/step - loss: 0.3193 - acc: 0.9500 - val_loss: 0.4350 - val_acc: 0.7500 Epoch 30/30
4/4 [==============================] - 9s 2s/step - loss: 0.3147 - acc: 0.8750 - val_loss: 0.5196 - val_acc: 0.8500 7.作图分析
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
使⽤数据增强的模型过拟合情况要好⼀些，验证准确率接近90%
8.使⽤测试集测试模型
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(150, 300),
batch_size=10,
class_mode='binary')
test_loss, test_acc = model.evaluate_generator(test_generator, steps=2)
print('test acc:', test_acc)
Found 20 images belonging to 2 classes.
test acc: 0.8999999761581421
9.使⽤模型预测图⽚
# 保存模型
# model.save('RMB_data_augmentation.h5')
# 导⼊保存的模型
dels import load_model
# model = load_model('RMB_data_augmentation.h5')
# 读取图⽚
import matplotlib.pyplot as plt
import matplotlib.image as mpimg # mpimg ⽤于读取图⽚
test_img = mpimg.imread('test_50.jpg')
plt.imshow(test_img)
plt.show()
test_img.shape
(221, 449, 3)
import cv2
test_img = size(test_img,(300,150))
plt.imshow(test_img)
plt.show()
test_img.shape
(150, 300, 3)
test_img = shape(1,150,300,3)
test_img.shape
(1, 150, 300, 3)
model.predict(test_img)
array([[0.9835865]], dtype=float32)
model.predict_classes(test_img)
test_img = mpimg.imread('test_20.jpg')
import cv2
test_img = size(test_img,(300,150))
plt.imshow(test_img)
plt.show()
test_img.shape
(150, 300, 3)
test_img = shape(1,150,300,3)
test_img.shape
(1, 150, 300, 3)
model.predict(test_img)
array([[3.184825e-08]], dtype=float32)
model.predict_classes(test_img)
array([[0]])
9.总结
使⽤数据增强的模型识别出了图像的类别
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