TensorFlow中的多层感知器介绍和用法示例

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多层感知器定义了人工神经网络最复杂的体系结构。它基本上由感知器的多层形成。 TensorFlow是由发行的非常流行的深度学习框架, 该笔记本将指导使用该库构建神经网络。如果我们想了解什么是多层感知器, 则必须使用Numpy从头开始开发多层感知器。
【TensorFlow中的多层感知器介绍和用法示例】多层感知器学习的图示如下所示：

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MLP网络用于监督学习格式。用于MLP网络的典型学习算法也称为反向传播算法。
多层感知器(MLP)是一种前馈人工神经网络, 可从一组输入生成一组输出。 MLP的特征在于, 输入节点的多层作为有向图连接在输入节点之间, 输入节点作为有向图在输入和输出层之间连接。 MLP使用反向传播来训练网络。 MLP是一种深度学习方法。
现在, 我们将重点放在针对图像分类问题的MLP实施上。

# Import MINST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot = True) import tensorflow as tf import matplotlib.pyplot as plt # Parameters learning_rate = 0.001 training_epochs = 20 batch_size = 100 display_step = 1 # Network Parameters n_hidden_1 = 256 # 1st layer num featuresn_hidden_2 = 256 # 2nd layer num 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]) # weights layer 1 h = tf.Variable(tf.random_normal([n_input, n_hidden_1])) # bias layer 1 bias_layer_1 = tf.Variable(tf.random_normal([n_hidden_1])) # layer 1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, h), bias_layer_1)) # weights layer 2 w = tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])) # bias layer 2 bias_layer_2 = tf.Variable(tf.random_normal([n_hidden_2])) # layer 2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, w), bias_layer_2)) # weights output layer output = tf.Variable(tf.random_normal([n_hidden_2, n_classes])) # biar output layer bias_output = tf.Variable(tf.random_normal([n_classes])) # output layer output_layer = tf.matmul(layer_2, output) + bias_output# cost function cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = output_layer, labels = y)) #cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output_layer, y)) # optimizer optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost) # optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(cost) # Plot settings avg_set = [] epoch_set = [] # 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 batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # Fit training using batch data sess.run(optimizer, feed_dict = {x: batch_xs, y: batch_ys}) # Compute average loss avg_cost += sess.run(cost, feed_dict = {x: batch_xs, y: batch_ys}) / total_batch# Display logs per epoch step if epoch % display_step == 0: print Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost)avg_set.append(avg_cost) epoch_set.append(epoch + 1)print "Training phase finished" plt.plot(epoch_set, avg_set, 'o', label = 'MLP Training phase') plt.ylabel('cost') plt.xlabel('epoch') plt.legend() plt.show() # Test model correct_prediction = tf.equal(tf.argmax(output_layer, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print "Model Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})

上面的代码行生成以下输出-