# Set up matplotlib fig, and size it to fit 4x4 pics fig = plt.gcf() fig.set_size_inches(ncols * 4, nrows * 4)
pic_index += 8 next_horse_pix = [os.path.join(train_horse_dir, fname) for fname in train_horse_names[pic_index-8:pic_index]] next_human_pix = [os.path.join(train_human_dir, fname) for fname in train_human_names[pic_index-8:pic_index]]
for i, img_path inenumerate(next_horse_pix+next_human_pix): # Set up subplot; subplot indices start at 1 sp = plt.subplot(nrows, ncols, i + 1) sp.axis('Off') # Don't show axes (or gridlines)
model = tf.keras.models.Sequential([ # Note the input shape is the desired size of the image 300x300 with 3 bytes color # This is the first convolution tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 3)), tf.keras.layers.MaxPooling2D(2, 2), # The second convolution tf.keras.layers.Conv2D(32, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # The third convolution tf.keras.layers.Conv2D(64, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # The fourth convolution tf.keras.layers.Conv2D(64, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # The fifth convolution tf.keras.layers.Conv2D(64, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # Flatten the results to feed into a DNN tf.keras.layers.Flatten(), # 512 neuron hidden layer tf.keras.layers.Dense(512, activation='relu'), # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans') tf.keras.layers.Dense(1, activation='sigmoid') ])
# 输出 模型各层参数的大概 model.summary()
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from tensorflow.keras.optimizers import RMSprop # 模型编译，指定loss损失函数，metrics评估标准 以及optimizer优化器 model.compile(loss='binary_crossentropy', optimizer=RMSprop(lr=0.001), metrics=['acc'])
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# All images will be rescaled by 1./255 # 利用ImageDataGenerator rescale 对读取图像进行正则化 归1 train_datagen = ImageDataGenerator(rescale=1/255) validation_datagen = ImageDataGenerator(rescale=1/255)
# Flow training images in batches of 128 using train_datagen generator # 创建训练样本生成器 ，指定训练目录，目录下不同image存放在 不同的 指定文件夹，并更具指定文件夹 自动设置label。 # 并通过设置target_size,在读取文件时 自动将image的shape 进行转换 train_generator = train_datagen.flow_from_directory( '/tmp/horse-or-human/', # This is the source directory for training images target_size=(300, 300), # All images will be resized to 150x150 batch_size=128, # Since we use binary_crossentropy loss, we need binary labels class_mode='binary')
# Flow training images in batches of 128 using train_datagen generator validation_generator = validation_datagen.flow_from_directory( '/tmp/validation-horse-or-human/', # This is the source directory for training images target_size=(300, 300), # All images will be resized to 150x150 batch_size=32, # Since we use binary_crossentropy loss, we need binary labels class_mode='binary')
import numpy as np import random from tensorflow.keras.preprocessing.image import img_to_array, load_img
# Let's define a new Model that will take an image as input, and will output # intermediate representations for all layers in the previous model after # the first. successive_outputs = [layer.output for layer in model.layers[1:]] #visualization_model = Model(img_input, successive_outputs) visualization_model = tf.keras.models.Model(inputs = model.input, outputs = successive_outputs) # Let's prepare a random input image from the training set. horse_img_files = [os.path.join(train_horse_dir, f) for f in train_horse_names] human_img_files = [os.path.join(train_human_dir, f) for f in train_human_names] img_path = random.choice(horse_img_files + human_img_files)
img = load_img(img_path, target_size=(300, 300)) # this is a PIL image x = img_to_array(img) # Numpy array with shape (150, 150, 3) x = x.reshape((1,) + x.shape) # Numpy array with shape (1, 150, 150, 3)
# Rescale by 1/255 x /= 255
# Let's run our image through our network, thus obtaining all # intermediate representations for this image. successive_feature_maps = visualization_model.predict(x)
# These are the names of the layers, so can have them as part of our plot layer_names = [layer.name for layer in model.layers]
# Now let's display our representations for layer_name, feature_map inzip(layer_names, successive_feature_maps): iflen(feature_map.shape) == 4: # Just do this for the conv / maxpool layers, not the fully-connected layers n_features = feature_map.shape[-1] # number of features in feature map # The feature map has shape (1, size, size, n_features) size = feature_map.shape[1] # We will tile our images in this matrix display_grid = np.zeros((size, size * n_features)) for i inrange(n_features): # Postprocess the feature to make it visually palatable x = feature_map[0, :, :, i] x -= x.mean() x /= x.std() x *= 64 x += 128 x = np.clip(x, 0, 255).astype('uint8') # We'll tile each filter into this big horizontal grid display_grid[:, i * size : (i + 1) * size] = x # Display the grid scale = 20. / n_features plt.figure(figsize=(scale * n_features, scale)) plt.title(layer_name) plt.grid(False) plt.imshow(display_grid, aspect='auto', cmap='viridis')
