MNIST手写数字识别的Keras实现

# coding=utf-8 # 兼容python3 from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import cv2 # 从tensorflow里导入keras和keras.layer from tensorflow import keras from tensorflow.keras import layers # 导入工具函数 from utils import * def inference(dtype): """ 使用keras定义mnist模型 """ # define a truncated_normal initializer tn_init = keras.initializers.truncated_normal(0, 0.1, SEED, dtype=dtype) # define a constant initializer const_init = keras.initializers.constant(0.1, dtype) # define a L2 regularizer l2_reg = keras.regularizers.l2(5e-4) """ 输入占位符。如果输入图像的shape是(28, 28, 1)，输入的一批图像(16张图)的shape 是(16, 28, 28, 1)；那么，在定义Input时，shape参数只需要一张图像的大小，也就 是(28, 28, 1)，而不是(16, 28, 28, 1)。 input placeholder. the Input's parameter shape should be a image's shape (28, 28, 1) rather than a batch of image's shape (16, 28, 28, 1). """ # inputs: shape(None, 28, 28, 1) inputs = layers.Input(shape=(IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS), dtype=dtype) """ 卷积，输出shape为(None, 28,18,32)。Conv2D的第一个参数为卷积核个数；第二个参数为卷积 核大小，和tensorflow不同的是，卷积核的大小只需指定卷积窗口的大小，例如在tensorflow中， 卷积核的大小为(BATCH_SIZE, 5, 5, 1)，那么在Keras中，只需指定卷积窗口的大小(5, 5)， 最后一维的大小会根据之前输入的形状自动推算，假如上一层的shape为(None, 28, 28, 1)，那 么最后一维的大小为1；第三个参数为strides，和上一个参数同理。其他参数可查阅Keras的官方文档。 """ # conv1: shape(None, 28, 28, 32) conv1 = layers.Conv2D(32, (5, 5), strides=(1, 1), padding='same', activation='relu', use_bias=True, kernel_initializer=tn_init, name='conv1')(inputs) # pool1: shape(None, 14, 14, 32) pool1 = layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='pool1')(conv1) # conv2: shape(None, 14, 14, 64) conv2 = layers.Conv2D(64, (5, 5), strides=(1, 1), padding='same', activation='relu', use_bias=True, kernel_initializer=tn_init, bias_initializer=const_init, name='conv2')(pool1) # pool2: shape(None, 7, 7, 64) pool2 = layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='pool2')(conv2) # flatten: shape(None, 3136) flatten = layers.Flatten(name='flatten')(pool2) # fc1: shape(None, 512) fc1 = layers.Dense(512, 'relu', True, kernel_initializer=tn_init, bias_initializer=const_init, kernel_regularizer=l2_reg, bias_regularizer=l2_reg, name='fc1')(flatten) # dropout dropout1 = layers.Dropout(0.5, seed=SEED)(fc1) # dense2: shape(None, 10) fc2 = layers.Dense(NUM_LABELS, activation=None, use_bias=True, kernel_initializer=tn_init, bias_initializer=const_init, name='fc2', kernel_regularizer=l2_reg, bias_regularizer=l2_reg)(dropout1) # softmax: shape(None, 10) softmax = layers.Softmax(name='softmax')(fc2) # make new model model = keras.Model(inputs=inputs, outputs=softmax, name='nmist') return model def main(argv=None): if argv.self_test: """ 为了测试模型是否可以运行，生成了一些随机数据集。 generate some fake data for testing model is functional. """ print('Running self-test...') # 生成训练集 # generate train dataset train_data, train_labels = fake_data(256) # 生成验证集 # generate validation dataset validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE) # 生成测试集 # generate test dataset test_data, test_labels = fake_data(EVAL_BATCH_SIZE) # 只训练一个epoch # training for only 1 epoch num_epochs = 1 else: """ 准备数据集。 prepare dataset. """ # 下载数据集 # Get the data. train_data_filename = maybe_download('train-images-idx3-ubyte.gz') train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz') test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz') test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz') # 把下载的数据解压为numpy数组 # Extract it into numpy arrays. train_data = extract_data(train_data_filename, 60000) train_labels = extract_labels(train_labels_filename, 60000) test_data = extract_data(test_data_filename, 10000) test_labels = extract_labels(test_labels_filename, 10000) # 分割train_data与train_labels，得到训练集以及验证集 # Generate a validation set. validation_data = train_data[:VALIDATION_SIZE, ...] validation_labels = train_labels[:VALIDATION_SIZE] train_data = train_data[VALIDATION_SIZE:, ...] train_labels = train_labels[VALIDATION_SIZE:] num_epochs = NUM_EPOCHS # 保存一下第一张图片，用来测试 # save the first image for testing img0 = test_data[0] # 因为test_data被缩放到了[-0.5, 0.5]，所以要恢复到原来的范围[0, 255] # rescale img0 from [-0.5, 0.5] to [0, 255] img0 = img0*PIXEL_DEPTH+PIXEL_DEPTH/2 # 保存 # save cv2.imwrite('test0.png', img0) # 对label进行one-hot编码，因为模型的最后一层有10个输出单元（10个类别） # one-hot encoding， because of last layer of model have 10 units(10 classes) train_labels = keras.utils.to_categorical(train_labels) validation_labels = keras.utils.to_categorical(validation_labels) test_labels = keras.utils.to_categorical(test_labels) # 获取模型 # make keras model model = inference(data_type(argv)) # 打印模型的信息 model.summary() # 编译模型；第一个参数是优化器；第二个参数为loss，因为是多元分类问题，固为 # 'categorical_crossentropy'；第三个参数为metrics，就是在训练的时候需 # 要监控的指标列表。 # compile model model.compile(optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.9, decay=1e-5), loss='categorical_crossentropy', metrics=['accuracy']) # 设置回调 # setting callbacks callbacks = [ # 把TensorBoard的日志写入文件夹'./logs' # write TensorBoard' logs to directory 'logs' keras.callbacks.TensorBoard(log_dir='./logs'), ] # 开始训练 # start training model.fit(train_data, train_labels, BATCH_SIZE, epochs=num_epochs, validation_data=(validation_data, validation_labels), callbacks=callbacks) # evaluate print('', 'evaluating on test sets...') loss, accuracy = model.evaluate(test_data, test_labels) print('test loss:', loss) print('test Accuracy:', accuracy) # save model model.save('mnist-model.h5') if __name__ == '__main__': # 定义parser parser = argparse.ArgumentParser() parser.add_argument( '--use_fp16', default=False, help='Use half floats instead of full floats if True.', action='store_true') parser.add_argument( '--self_test', default=False, action='store_true', help='True if running a self test.') # 解析参数 # parse arguments FLAGS, unparsed = parser.parse_known_args() # 调用主函数 # call main() main(FLAGS)