數(shù)據(jù)集鏈接：https://download.csdn.net/download/fanzonghao/10598333

from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("./mnist/", one_hot=True)import tensorflow as tf# Parameters learning_rate = 0.001 training_epochs = 30 batch_size = 100 display_step = 1# Network Parameters n_hidden_1 = 256 # 1st layer number of features n_hidden_2 = 512 # 2nd layer number of features n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits)# tf Graph input x = tf.placeholder("float", [None, n_input]) y = tf.placeholder("float", [None, n_classes])# Create model def multilayer_perceptron(x, weights, biases):# Hidden layer with RELU activationlayer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])layer_1 = tf.nn.relu(layer_1)# Hidden layer with RELU activationlayer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])layer_2 = tf.nn.relu(layer_2)# layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])# layer_3 = tf.nn.relu(layer_3)#we can add dropout layer# drop_out = tf.nn.dropout(layer_2, 0.75)# Output layer with linear activationout_layer = tf.matmul(layer_2, weights['out']) + biases['out']return out_layer# Store layers weight & biases weights = {#you can change'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),#'h3': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) } biases = {'b1': tf.Variable(tf.random_normal([n_hidden_1])),'b2': tf.Variable(tf.random_normal([n_hidden_2])),#'b3': tf.Variable(tf.random_normal([n_hidden_2])),'out': tf.Variable(tf.random_normal([n_classes])) }# Construct model pred = multilayer_perceptron(x, weights, biases)# Define loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Initializing the variables init = tf.global_variables_initializer()# Launch the graph with tf.Session() as sess:sess.run(init)# Training cyclefor epoch in range(training_epochs):avg_cost = 0.total_batch = int(mnist.train.num_examples/batch_size)# Loop over all batchesfor i in range(total_batch):batch_x, batch_y = mnist.train.next_batch(batch_size)# Run optimization op (backprop) and cost op (to get loss value)_, c = sess.run([optimizer, cost], feed_dict={x: batch_x,y: batch_y})# Compute average lossavg_cost += c / total_batch# Display logs per epoch stepif epoch % display_step == 0:print("Epoch:", '%04d' % (epoch+1), "cost=", \"{:.9f}".format(avg_cost))print("Optimization Finished!")# Test modelcorrect_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))# Calculate accuracyaccuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

打印結(jié)果：

總結(jié)

以上是生活随笔為你收集整理的三层神经网络实现手写数字的识别（基于tensorflow）的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

如果覺(jué)得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。