卷积神经网络(二)
本例使用神经网络识别人和马的照片
第一步：下载数据
wget --no-check-certificate \ https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \ -O /tmp/horse-or-human.zip
运行结果
--2020-01-03 06:15:15-- https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.111.128, 2607:f8b0:4001:c15::80 Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.111.128|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 149574867 (143M) [application/zip] Saving to: ‘/tmp/horse-or-human.zip’ /tmp/horse-or-human 100%[===================>] 142.65M 98.5MB/s in 1.4s 2020-01-03 06:15:16 (98.5 MB/s) - ‘/tmp/horse-or-human.zip’ saved [149574867/149574867]
第二步：解压数据
import os import zipfile local_zip = '/tmp/horse-or-human.zip' zip_ref = zipfile.ZipFile(local_zip, 'r') zip_ref.extractall('/tmp/horse-or-human') zip_ref.close()
第三步：定义训练集
# Directory with our training horse pictures train_horse_dir = os.path.join('/tmp/horse-or-human/horses') # Directory with our training human pictures train_human_dir = os.path.join('/tmp/horse-or-human/humans')
注释：可以使用 os.listdir 进行查看
train_horse_names = os.listdir(train_horse_dir) print(train_horse_names[:10]) train_human_names = os.listdir(train_human_dir) print(train_human_names[:10])
运行结果
['horse38-3.png', 'horse03-0.png', 'horse35-8.png', 'horse04-6.png', 'horse44-6.png', 'horse49-8.png', 'horse14-9.png', 'horse06-5.png', 'horse41-5.png', 'horse04-9.png']
['human14-16.png', 'human15-15.png', 'human11-10.png', 'human10-13.png', 'human07-06.png', 'human17-01.png', 'human09-13.png', 'human09-11.png', 'human05-15.png', 'human04-14.png']
注释： 查看目录中有多少个数目
print('total training horse images:', len(os.listdir(train_horse_dir))) print('total training human images:', len(os.listdir(train_human_dir)))
运行结果
total training horse images: 500
total training human images: 527
注释： 可以使用 matplotlib 进行照片查看
%matplotlib inline import matplotlib.pyplot as plt import matplotlib.image as mpimg # Parameters for our graph; we'll output images in a 4x4 configuration nrows = 4 ncols = 4 # Index for iterating over images pic_index = 0 # Set up matplotlib fig, and size it to fit 4x4 pics fig = plt.gcf() fig.set_size_inches(ncols * 4, nrows * 4)
# 8 * 8 列的 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 in enumerate(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) img = mpimg.imread(img_path) plt.imshow(img) plt.show()
运行结果
第四步：定义模型与网络
import tensorflow as tf 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') ])
注释： 可以使用 model.summary() 方法进行查看
model.summary()
运行结果
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 298, 298, 16) 448 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 149, 149, 16) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 147, 147, 32) 4640 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 73, 73, 32) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 71, 71, 64) 18496 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 33, 33, 64) 36928 _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 16, 16, 64) 0 _________________________________________________________________ conv2d_4 (Conv2D) (None, 14, 14, 64) 36928 _________________________________________________________________ max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64) 0 _________________________________________________________________ flatten (Flatten) (None, 3136) 0 _________________________________________________________________ dense (Dense) (None, 512) 1606144 _________________________________________________________________ dense_1 (Dense) (None, 1) 513 ================================================================= Total params: 1,704,097 Trainable params: 1,704,097 Non-trainable params: 0 _________________________________________________________________
第五步：编译模型
from tensorflow.keras.optimizers import RMSprop model.compile(loss='binary_crossentropy', optimizer=RMSprop(lr=0.001), metrics=['acc'])
第六步： 处理数据
from tensorflow.keras.preprocessing.image import ImageDataGenerator # All images will be rescaled by 1./255 train_datagen = ImageDataGenerator(rescale=1/255) # Flow training images in batches of 128 using train_datagen generator 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')
第七步： 训练模型
history = model.fit_generator( train_generator, steps_per_epoch=8, epochs=15, verbose=1)
运行结果
Epoch 1/15 8/8 [==============================] - 8s 1s/step - loss: 1.7235 - acc: 0.5006 Epoch 2/15 8/8 [==============================] - 6s 689ms/step - loss: 0.6713 - acc: 0.6104 Epoch 3/15 8/8 [==============================] - 5s 580ms/step - loss: 0.7703 - acc: 0.6809 Epoch 4/15 8/8 [==============================] - 5s 679ms/step - loss: 0.5567 - acc: 0.6463 Epoch 5/15 8/8 [==============================] - 5s 681ms/step - loss: 1.3130 - acc: 0.7119 Epoch 6/15 8/8 [==============================] - 5s 680ms/step - loss: 0.3521 - acc: 0.8832 Epoch 7/15 8/8 [==============================] - 6s 772ms/step - loss: 0.1969 - acc: 0.9258 Epoch 8/15 8/8 [==============================] - 5s 678ms/step - loss: 0.1722 - acc: 0.9244 Epoch 9/15 8/8 [==============================] - 5s 674ms/step - loss: 0.1337 - acc: 0.9444 Epoch 10/15 8/8 [==============================] - 5s 678ms/step - loss: 0.5653 - acc: 0.8409 Epoch 11/15 8/8 [==============================] - 6s 779ms/step - loss: 0.4015 - acc: 0.8350 Epoch 12/15 8/8 [==============================] - 6s 688ms/step - loss: 0.1242 - acc: 0.9633 Epoch 13/15 8/8 [==============================] - 5s 597ms/step - loss: 0.0500 - acc: 0.9742 Epoch 14/15 8/8 [==============================] - 6s 689ms/step - loss: 0.0549 - acc: 0.9800 Epoch 15/15 8/8 [==============================] - 6s 798ms/step - loss: 0.3984 - acc: 0.8818
运行模型， 从本地文件中上传照片
import numpy as np from google.colab import files from keras.preprocessing import image uploaded = files.upload() for fn in uploaded.keys(): # predicting images path = '/content/' + fn img = image.load_img(path, target_size=(300, 300)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) images = np.vstack([x]) classes = model.predict(images, batch_size=10) print(classes[0]) if classes[0]>0.5: print(fn + " is a human") else: print(fn + " is a horse")
查看运行过程
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 in zip(layer_names, successive_feature_maps): if len(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 in range(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')
第八步：清除数据
import os, signal os.kill(os.getpid(), signal.SIGKILL)
