如何使用TensorFlow创建CNN

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使用TensorFlow创建CNN

# -*- coding:utf-8 -*-import tensorflow as tfimport numpy as np# 下载mnist数据集from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('./mnist_data/', one_hot=True)# from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets## mnist = read_data_sets('./mnist_data/', one_hot=True)n_output_layer = 10# 定义待训练的神经网络def convolutional_neural_network(data):    weights = {'w_conv1': tf.Variable(tf.random_normal([5, 5, 1, 32])),               'w_conv2': tf.Variable(tf.random_normal([5, 5, 32, 64])),               'w_fc': tf.Variable(tf.random_normal([7 * 7 * 64, 1024])),               'out': tf.Variable(tf.random_normal([1024, n_output_layer]))}    biases = {'b_conv1': tf.Variable(tf.random_normal([32])),              'b_conv2': tf.Variable(tf.random_normal([64])),              'b_fc': tf.Variable(tf.random_normal([1024])),              'out': tf.Variable(tf.random_normal([n_output_layer]))}    data = tf.reshape(data, [-1, 28, 28, 1])    conv1 = tf.nn.relu(        tf.add(tf.nn.conv2d(data, weights['w_conv1'], strides=[1, 1, 1, 1], padding='SAME'), biases['b_conv1']))    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')    conv2 = tf.nn.relu(        tf.add(tf.nn.conv2d(conv1, weights['w_conv2'], strides=[1, 1, 1, 1], padding='SAME'), biases['b_conv2']))    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')    fc = tf.reshape(conv2, [-1, 7 * 7 * 64])    fc = tf.nn.relu(tf.add(tf.matmul(fc, weights['w_fc']), biases['b_fc']))    # dropout剔除一些"神经元"    # fc = tf.nn.dropout(fc, 0.8)    output = tf.add(tf.matmul(fc, weights['out']), biases['out'])    return output# 每次使用100条数据进行训练batch_size = 100X = tf.placeholder('float', [None, 28 * 28])Y = tf.placeholder('float')# 使用数据训练神经网络def train_neural_network(X, Y):    predict = convolutional_neural_network(X)    # cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict,labels=Y))    cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=predict, labels=Y))    optimizer = tf.train.AdamOptimizer().minimize(cost_func)  # learning rate 默认 0.001    epochs = 1    with tf.Session() as session:        # session.run(tf.initialize_all_variables())        session.run(tf.global_variables_initializer())        epoch_loss = 0        for epoch in range(epochs):            for i in range(int(mnist.train.num_examples / batch_size)):                x, y = mnist.train.next_batch(batch_size)                _, c = session.run([optimizer, cost_func], feed_dict={X: x, Y: y})                epoch_loss += c            print(epoch, ' : ', epoch_loss)        correct = tf.equal(tf.argmax(predict, 1), tf.argmax(Y, 1))        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))        print('准确率: ', accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}))train_neural_network(X, Y)

执行结果：

准确率:  0.9789

tflearn

下面使用tflearn重写上面代码，tflearn是TensorFlow的高级封装，类似Keras。

tflearn提供了更简单、直观的接口。和scikit-learn差不多，代码如下：

# -*- coding:utf-8 -*-import tflearnfrom tflearn.layers.conv import conv_2d, max_pool_2dfrom tflearn.layers.core import input_data, dropout, fully_connectedfrom tflearn.layers.estimator import regressiontrain_x, train_y, test_x, test_y = tflearn.datasets.mnist.load_data(    data_dir="./mnist_data/",one_hot=True)train_x = train_x.reshape(-1, 28, 28, 1)test_x = test_x.reshape(-1, 28, 28, 1)# 定义神经网络模型conv_net = input_data(shape=[None, 28, 28, 1], name='input')conv_net = conv_2d(conv_net, 32, 2, activation='relu')conv_net = max_pool_2d(conv_net, 2)conv_net = conv_2d(conv_net, 64, 2, activation='relu')conv_net = max_pool_2d(conv_net, 2)conv_net = fully_connected(conv_net, 1024, activation='relu')conv_net = dropout(conv_net, 0.8)conv_net = fully_connected(conv_net, 10, activation='softmax')conv_net = regression(conv_net, optimizer='adam', loss='categorical_crossentropy', name='output')model = tflearn.DNN(conv_net)# 训练model.fit({'input': train_x}, {'output': train_y}, n_epoch=13,          validation_set=({'input': test_x}, {'output': test_y}),          snapshot_step=300, show_metric=True, run_id='mnist')model.save('./mnist.model')  # 保存模型"""model.load('mnist.model')   # 加载模型model.predict([test_x[1]])  # 预测"""

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