Coding according to TensorFlow 官方文档中文版
1 import tensorflow as tf 2 from tensorflow.examples.tutorials.mnist import input_data 3 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 4 5 6 ''' Intro. for this python file. 7 Objective: 8 Implement for a Softmax Regression Model on MNIST. 9 Operating Environment: 10 python = 3.6.4 11 tensorflow = 1.5.0 12 ''' 13 14 15 # Set a placeholder. We hope arbitrary number of images could be input to this model. 16 x = tf.placeholder("float", [None, 784]) 17 18 # Set weight/bias variables. Their initial values could be set Randomly. 19 W = tf.Variable(tf.zeros([784, 10])) 20 b = tf.Variable(tf.zeros([10])) 21 22 # Model implementation 23 y = tf.nn.softmax(tf.matmul(x, W) + b) 24 25 # Set a placeholder 'y_' to accept the ground-truth values. 26 y_ = tf.placeholder("float", [None, 10]) 27 28 # Calculate cross-entropy 29 cross_entropy = -tf.reduce_sum(y_ * tf.log(y)) 30 31 # Train Softmax Regression Model 32 train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) 33 34 # Initialize variables 35 # init = tf.initialize_all_variables() # Warning 36 init = tf.global_variables_initializer() 37 38 # Launch the graph in a session. 39 sess = tf.Session() 40 sess.run(init) 41 42 for i in range(1000): 43 batch_xs, batch_ys = mnist.train.next_batch(100) # Grabbing 100 batch data points from training data randomly. 44 sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) 45 46 47 # Model Evaluation 48 ''' tf.argmax(input, axis=None, name=None, dimension=None, output_type=tf.int64) 49 Explanation: 50 Returns the index with the largest value across axes of a tensor. 51 test = np.array([[1, 2, 3], [2, 3, 4], [5, 4, 3], [8, 7, 2]]) 52 np.argmax(test, 0) # output：array([3, 3, 1]) 53 np.argmax(test, 1) # output：array([2, 2, 0, 0]) 54 Returns: 55 A Tensor of type output_type. 56 ''' 57 58 59 # correct_prediction = tf.equal(tf.arg_max(y, 1), tf.arg_max(y_, 1)) # Warning 60 correct_prediction = tf.equal(tf.argmax(y, axis=1), tf.argmax(y_, axis=1)) 61 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 62 print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) 63 64 # The result is around 0.91.
