吴裕雄--天生自然神经网络与深度学习实战Python+Keras+TensorFlow：用预先训练好的卷积网络实现图像快速识别

from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
import os
import matplotlib.pyplot as plt

datagen = ImageDataGenerator(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')

fnames = [os.path.join(train_dogs_dir, fname) for fname in os.listdir(train_dogs_dir)]


#从狗图片中选择一张
img_path = fnames[3]
print(img_path)

img = image.load_img(img_path, target_size=(150,150))
img = image.img_to_array(img)
#把img变成3维向量形式[1,150,150]
img = img.reshape((1,) + img.shape)
i = 0
#对图片做输入参数中指定的6种变换
f, ax = plt.subplots(1, 6)
for batch in datagen.flow(img, batch_size=1):
    imgplot = ax[i].imshow(image.array_to_img(batch[0]))
    ax[i].axis('off')
    i += 1
    if i % 6 == 0:
        break
plt.show()

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.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation="sigmoid"))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['acc'])
model.summary()

#执行这里的代码时，记住把网络构造部分的代码再执行一次以便确保我们训练的是一个新网络
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,)
train_generator = train_datagen.flow(training_set, train_labels, batch_size = 32)
validation_datagen = ImageDataGenerator(rescale = 1. / 255)
validation_generator = validation_datagen.flow(validation_set, validation_labels, batch_size = 32)
history = model.fit_generator(train_generator, steps_per_epoch=100,
                             epochs = 30, validation_data = validation_generator,
                             validation_steps = 50)

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(1, len(acc) + 1)

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()

from keras.preprocessing import image

img_path = os.path.join(train_dogs_dir, 'dog.1.jpg')
img = image.load_img(img_path, target_size=(150,150))
img_tensor = image.img_to_array(img)
img_tensor = np.expand_dims(img_tensor, axis=0)
img_tensor /= 255.
print(img_tensor.shape)
plt.imshow(img_tensor[0])

from keras.models import Model
layer_outputs = [layer.output for layer in model.layers[:8]]
activation_model = Model(inputs=model.input, outputs=layer_outputs)
activations = activation_model.predict(img_tensor)

layer_show = 0
channel = 18

layer_activation = activations[layer_show]
plt.matshow(layer_activation[0,:,:,channel], cmap='viridis')

fig, ax = plt.subplots(2, 4)
plt.axis('off')

for i in range(0, 2):
    for j in range(0, 4):
        layer_activation = activations[i*4 + j]
        ax[i, j].matshow(layer_activation[0,:,:,9], cmap='viridis')
        ax[i, j].set_xticks([])
        ax[i, j].set_yticks([])