tensorflow入门：CNN for MNIST
本文共 10490 字，大约阅读时间需要 34 分钟。
理论部分不再赘述，完整的代码如下：
import tensorflow as tfimport numpy as npfrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)# hyperparameterlearning_rate = 0.001training_epoches = 20batch_size = 100class Model: def __init__(self, sess, name): self.sess = sess self.name = name self._build_net() def _build_net(self): # with tf.variable_scope(self.name): self.training = tf.placeholder(tf.bool) # input placeholder for X & Y self.X = tf.placeholder(tf.float32, [None, 784]) self.Y = tf.placeholder(tf.float32, [None, 10]) # img 28x28x1 (black/white) X_img = tf.reshape(self.X, [-1, 28, 28, 1]) # convolutional layer 1 & pooling layer 1 conv1 = tf.layers.conv2d(inputs=X_img, filters=32, kernel_size=[3, 3], padding="SAME", activation=tf.nn.relu) pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], padding="SAME", strides=2) dropout1 = tf.layers.dropout(inputs=pool1, rate=0.3, training=self.training) # convolutional layer 2 & pooling layer 2 conv2 = tf.layers.conv2d(inputs=dropout1, filters=64, kernel_size=[3, 3], padding="SAME", activation=tf.nn.relu) pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], padding="SAME", strides=2) dropout2 = tf.layers.dropout(inputs=pool2, rate=0.3, training=self.training) # convolutional layer 3 & pooling layer 3 conv3 = tf.layers.conv2d(inputs=dropout2, filters=128, kernel_size=[3, 3], padding="SAME", activation=tf.nn.relu) pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], padding="SAME", strides=2) dropout3 = tf.layers.dropout(inputs=pool3, rate=0.3, training=self.training) # dense layer with Relu flat = tf.reshape(dropout3, [-1, 128 * 4 * 4]) dense4 = tf.layers.dense(inputs=flat, units=625, activation=tf.nn.relu) dropout4 = tf.layers.dropout(inputs=dense4, rate=0.5, training=self.training) # FC layer 625 input -> 10 output, no activation function self.logits = tf.layers.dense(inputs=dropout4, units=10) # define loss & optimizer self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2( logits = self.logits, labels=self.Y)) self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.cost) # accuracy correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.Y, 1)) self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) def train(self, x_data, y_data, training=True): return self.sess.run([self.cost, self.optimizer], feed_dict={ self.X: x_data, self.Y: y_data, self.training: training}) def predict(self, x_test, training=False): return self.sess.run(self.logits, feed_dict={ self.X :x_test, self.training: training}) def get_accuracy(self, x_test, y_test, training=False): return self.sess.run(self.accuracy, feed_dict={ self.X: x_test,self.Y: y_test, self.training: training}) # train the modelswith tf.Session() as sess: models = [] num_models = 2 for m in range(num_models): models.append(Model(sess, "modal"+str(m))) sess.run(tf.global_variables_initializer()) print('Learning Start!') for epoch in range(training_epoches): avg_cost_list = np.zeros(len(models)) total_batch = int(mnist.train.num_examples / batch_size) for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # train each modal for m_id, m in enumerate(models): c, _ = m.train(batch_xs, batch_ys) avg_cost_list[m_id] += c / total_batch print('Epoch: ', '%04d' %(epoch+1), 'cost=', avg_cost_list) print('Learning finished!') # test & accuracy test_size = len(mnist.test.labels) predictions = np.zeros([test_size, 10]) for m_id,m in enumerate(models): print(m_id, "Accuracy:", m.get_accuracy(mnist.test.images, mnist.test.labels)) p = m.predict(mnist.test.images) predictions += p ensemble_correct_prediction = tf.equal(tf.argmax(predictions, 1), tf.argmax(mnist.test.labels, 1)) ensemble_accuracy = tf.reduce_mean(tf.cast(ensemble_correct_prediction, tf.float32)) print("Ensemble_accuracy:", sess.run(ensemble_accuracy)) 结果：
Learning Start!Epoch: 0001 cost= [0.29211415 0.28355632]Epoch: 0002 cost= [0.08716567 0.0870499 ]Epoch: 0003 cost= [0.06902521 0.06623169]Epoch: 0004 cost= [0.05563359 0.05452387]Epoch: 0005 cost= [0.04963774 0.04871382]Epoch: 0006 cost= [0.04462749 0.04449957]Epoch: 0007 cost= [0.04132144 0.03907955]Epoch: 0008 cost= [0.03792324 0.03861412]Epoch: 0009 cost= [0.0354344 0.03323769]Epoch: 0010 cost= [0.03516847 0.03405525]Epoch: 0011 cost= [0.03143759 0.03219781]Epoch: 0012 cost= [0.03051504 0.02993162]Epoch: 0013 cost= [0.02906878 0.02711077]Epoch: 0014 cost= [0.02729127 0.02754832]Epoch: 0015 cost= [0.02729633 0.02632647]Epoch: 0016 cost= [0.02438517 0.02701174]Epoch: 0017 cost= [0.02482958 0.0244114 ]Epoch: 0018 cost= [0.02455271 0.02649499]Epoch: 0019 cost= [0.02371975 0.02178147]Epoch: 0020 cost= [0.02260135 0.0213784 ]Learning finished!0 Accuracy: 0.9951 Accuracy: 0.9949Ensemble_accuracy: 0.9954
结果前面的其实有很长的warning，这里没有给出。warning是说新版本的tensorflow把mnist数据集移动到了别的地方，建议你从别的地方导入进来。这篇博文仅做例子。实际使用tensorflow的时候，你都是自己写读取数据的函数什么的，需要根据数据集的存储格式写不同的Python代码。
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