石头减到布
建立一个可以有多个分类的神经网络
第一步：下载数据
wget --no-check-certificate \ https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps.zip \ -O tmp/rps.zip wget --no-check-certificate \ https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps-test-set.zip \ -O tmp/rps-test-set.zip
第二步：解压并显示数据
import os import zipfile # unzip file local_zip = 'tmp/rps.zip' zip_ref = zipfile.ZipFile(local_zip, 'r') zip_ref.extractall('tmp/') zip_ref.close() local_zip = 'tmp/rps-test-set.zip' zip_ref = zipfile.ZipFile(local_zip, 'r') zip_ref.extractall('tmp/') zip_ref.close() rock_dir = os.path.join('tmp/rps/rock') paper_dir = os.path.join('tmp/rps/paper') scissors_dir = os.path.join('tmp/rps/scissors') print('total training rock images:', len(os.listdir(rock_dir))) print('total training paper images:', len(os.listdir(paper_dir))) print('total training scissors images:', len(os.listdir(scissors_dir))) rock_files = os.listdir(rock_dir) print(rock_files[:10]) paper_files = os.listdir(paper_dir) print(paper_files[:10]) scissors_files = os.listdir(scissors_dir) print(scissors_files[:10])
运行结果：
total training rock images: 840 total training paper images: 840 total training scissors images: 840 ['rock04-059.png', 'rock01-108.png',
'rock04-065.png', 'rock05ck01-067.png',
'rock05ck01-073.png', 'rock04-071.png',
'rock05ck01-098.png', 'rock02-008.png',
'rock07-k03-013.png', 'rock02-034.png'] ['paper03-088.png', 'paper05-026.png', 'paper05-032.png',
'paper03-077.png', 'paper03-063.png', 'paper02-099.png',
'paper04-037.png', 'paper04-023.png', 'paper02-066.png', 'paper02-072.png'] ['testscissors03-040.png', 'testscissors03-054.png', 'testscissors03-068.png',
'testscissors03-083.png', 'testscissors03-097.png', 'scissors03-113.png',
'scissors03-107.png', 'testscissors02-051.png', 'testscissors02-045.png', 'scissors01-002.png']
注释： 使用 matplotlib 显示数据
%matplotlib inline import matplotlib.pyplot as plt import matplotlib.image as mpimg pic_index = 2 next_rock = [os.path.join(rock_dir, fname) for fname in rock_files[pic_index-2:pic_index]] next_paper = [os.path.join(paper_dir, fname) for fname in paper_files[pic_index-2:pic_index]] next_scissors = [os.path.join(scissors_dir, fname) for fname in scissors_files[pic_index-2:pic_index]] for i, img_path in enumerate(next_rock+next_paper+next_scissors): #print(img_path) img = mpimg.imread(img_path) plt.imshow(img) plt.axis('Off') plt.show()
第三步：建立模型
import tensorflow as tf import keras_preprocessing from keras_preprocessing import image from keras_preprocessing.image import ImageDataGenerator TRAINING_DIR = "tmp/rps/" training_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') VALIDATION_DIR = "tmp/rps-test-set/" validation_datagen = ImageDataGenerator(rescale = 1./255) train_generator = training_datagen.flow_from_directory( TRAINING_DIR, target_size=(150,150), class_mode='categorical' ) validation_generator = validation_datagen.flow_from_directory( VALIDATION_DIR, target_size=(150,150), class_mode='categorical' ) model = tf.keras.models.Sequential([ # Note the input shape is the desired size of the image 150x150 with 3 bytes color # This is the first convolution tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(150, 150, 3)), tf.keras.layers.MaxPooling2D(2, 2), # The second convolution tf.keras.layers.Conv2D(64, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # The third convolution tf.keras.layers.Conv2D(128, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # The fourth convolution tf.keras.layers.Conv2D(128, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # Flatten the results to feed into a DNN tf.keras.layers.Flatten(), tf.keras.layers.Dropout(0.5), # 512 neuron hidden layer tf.keras.layers.Dense(512, activation='relu'), tf.keras.layers.Dense(3, activation='softmax') ]) model.summary() model.compile(loss = 'categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) history = model.fit_generator(train_generator, epochs=25, validation_data = validation_generator, verbose = 1) model.save("rps.h5")
运行结果：
Found 2520 images belonging to 3 classes. Found 372 images belonging to 3 classes. Model: "sequential_5" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_20 (Conv2D) (None, 148, 148, 64) 1792 _________________________________________________________________ max_pooling2d_20 (MaxPooling (None, 74, 74, 64) 0 _________________________________________________________________ conv2d_21 (Conv2D) (None, 72, 72, 64) 36928 _________________________________________________________________ max_pooling2d_21 (MaxPooling (None, 36, 36, 64) 0 _________________________________________________________________ conv2d_22 (Conv2D) (None, 34, 34, 128) 73856 _________________________________________________________________ max_pooling2d_22 (MaxPooling (None, 17, 17, 128) 0 _________________________________________________________________ conv2d_23 (Conv2D) (None, 15, 15, 128) 147584 _________________________________________________________________ max_pooling2d_23 (MaxPooling (None, 7, 7, 128) 0 _________________________________________________________________ flatten_5 (Flatten) (None, 6272) 0 _________________________________________________________________ dropout_5 (Dropout) (None, 6272) 0 _________________________________________________________________ dense_10 (Dense) (None, 512) 3211776 _________________________________________________________________ dense_11 (Dense) (None, 3) 1539 ================================================================= Total params: 3,473,475 Trainable params: 3,473,475 Non-trainable params: 0 _________________________________________________________________ Epoch 1/25 79/79==============================] - 20s 257ms/step - loss: 1.2037 - acc: 0.3786 - val_loss: 1.0084 - val_acc: 0.6344 Epoch 2/25 79/79==============================] - 19s 242ms/step - loss: 0.8781 - acc: 0.6000 - val_loss: 0.3174 - val_acc: 0.9946 Epoch 3/25 79/79==============================] - 20s 250ms/step - loss: 0.5636 - acc: 0.7595 - val_loss: 0.1306 - val_acc: 1.0000 Epoch 4/25 79/79==============================] - 19s 243ms/step - loss: 0.4033 - acc: 0.8397 - val_loss: 0.2548 - val_acc: 0.8414 Epoch 5/25 79/79==============================] - 19s 240ms/step - loss: 0.3114 - acc: 0.8897 - val_loss: 0.0518 - val_acc: 0.9866 Epoch 6/25 79/79==============================] - 20s 251ms/step - loss: 0.2161 - acc: 0.9278 - val_loss: 0.1461 - val_acc: 0.9409 Epoch 7/25 79/79==============================] - 19s 246ms/step - loss: 0.2107 - acc: 0.9282 - val_loss: 0.0579 - val_acc: 0.9892 Epoch 8/25 79/79==============================] - 19s 243ms/step - loss: 0.1796 - acc: 0.9345 - val_loss: 0.0487 - val_acc: 0.9812 Epoch 9/25 79/79==============================] - 20s 252ms/step - loss: 0.1681 - acc: 0.9468 - val_loss: 0.0209 - val_acc: 0.9946 Epoch 10/25 79/79==============================] - 20s 257ms/step - loss: 0.1286 - acc: 0.9532 - val_loss: 0.1703 - val_acc: 0.9328 Epoch 11/25 79/79==============================] - 19s 241ms/step - loss: 0.1457 - acc: 0.9532 - val_loss: 0.0287 - val_acc: 0.9919 Epoch 12/25 79/79==============================] - 19s 241ms/step - loss: 0.1053 - acc: 0.9687 - val_loss: 0.0737 - val_acc: 0.9704 Epoch 13/25 79/79==============================] - 20s 249ms/step - loss: 0.1216 - acc: 0.9603 - val_loss: 0.0290 - val_acc: 0.9866 Epoch 14/25 79/79==============================] - 19s 236ms/step - loss: 0.1651 - acc: 0.9532 - val_loss: 0.0918 - val_acc: 0.9758 Epoch 15/25 79/79==============================] - 19s 236ms/step - loss: 0.0996 - acc: 0.9659 - val_loss: 0.1870 - val_acc: 0.8978 Epoch 16/25 79/79==============================] - 20s 248ms/step - loss: 0.0804 - acc: 0.9702 - val_loss: 0.0228 - val_acc: 0.9839 Epoch 17/25 79/79==============================] - 19s 241ms/step - loss: 0.1079 - acc: 0.9659 - val_loss: 0.0448 - val_acc: 0.9785 Epoch 18/25 79/79==============================] - 19s 240ms/step - loss: 0.1005 - acc: 0.9667 - val_loss: 0.0431 - val_acc: 0.9812 Epoch 19/25 79/79==============================] - 19s 244ms/step - loss: 0.0983 - acc: 0.9698 - val_loss: 0.0627 - val_acc: 0.9785 Epoch 20/25 79/79==============================] - 20s 249ms/step - loss: 0.0830 - acc: 0.9738 - val_loss: 0.3355 - val_acc: 0.8575 Epoch 21/25 79/79==============================] - 19s 245ms/step - loss: 0.1141 - acc: 0.9647 - val_loss: 0.0584 - val_acc: 0.9731 Epoch 22/25 79/79==============================] - 19s 243ms/step - loss: 0.0803 - acc: 0.9750 - val_loss: 0.0380 - val_acc: 0.9812 Epoch 23/25 79/79==============================] - 20s 253ms/step - loss: 0.0762 - acc: 0.9754 - val_loss: 0.3842 - val_acc: 0.9167 Epoch 24/25 79/79==============================] - 19s 246ms/step - loss: 0.0781 - acc: 0.9758 - val_loss: 0.0176 - val_acc: 0.9892 Epoch 25/25 79/79==============================] - 19s 237ms/step - loss: 0.0708 - acc: 0.9810 - val_loss: 0.1145 - val_acc: 0.9543
使用图像显示
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, 'r', label='Training accuracy') plt.plot(epochs, val_acc, 'b', label='Validation accuracy') plt.title('Training and validation accuracy') plt.legend(loc=0) plt.figure() plt.show()
运行结果
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