import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data
# Loading data set MNIST = input_data.read_data_sets ( " MNIST_data " , one_hot = True) # batch size the batch_size = 64 # calculates a total period of a number of batches n_batch mnist.train.num_examples // = the batch_size # define two placeholder X = tf.placeholder (tf.float32, [None, 784 ]) Y = tf.placeholder (tf.float32, [None, 10 ]) # Create a simple neural network: 784-10 W is = tf.Variable ( tf.truncated_normal ([784,10], STDDEV = 0.1 )) B = tf.Variable (tf.zeros ([10]) + 0.1 ) Prediction = tf.nn.softmax (tf.matmul (X, W is) + B ) # Quadratic cost function # Loss = tf.losses.mean_squared_error (Y, Prediction) # Cross Entropy Loss = tf.losses.softmax_cross_entropy (Y, Prediction) # using a gradient descent method Train tf.train.GradientDescentOptimizer = (0.3 ) .minimize ( Loss) # store the result in a Boolean list correct_prediction = tf.equal (tf.argmax (Y,. 1), tf.argmax (Prediction,. 1 )) # required accuracy accuracy = tf.reduce_mean (tf.cast (correct_prediction , tf.float32)) with tf.Session () AS sess: # variable initialization sess.run (tf.global_variables_initializer ()) # cycles epoch: trained once all the data is a cycle for Epoch in the Range (21): For BATCH in Range (n_batch): # access to data and a tag batch batch_xs, batch_ys = mnist.train.next_batch (the batch_size) sess.run (Train, feed_dict = {X: batch_xs, Y: batch_ys}) # each training cycle do a test ACC = sess.run (Accuracy, feed_dict = {X: mnist.test.images, Y: mnist.test.labels}) Print ( " Iter " + STR (Epoch) + " , the Accuracy testing " + str (the ACC))
Extracting MNIST_data\train-images-idx3-ubyte.gz Extracting MNIST_data\train-labels-idx1-ubyte.gz Extracting MNIST_data\t10k-images-idx3-ubyte.gz Extracting MNIST_data\t10k-labels-idx1-ubyte.gz Iter 0,Testing Accuracy 0.7473 Iter 1,Testing Accuracy 0.8413 Iter 2,Testing Accuracy 0.9066 Iter 3,Testing Accuracy 0.9113 Iter 4,Testing Accuracy 0.9143 Iter 5,Testing Accuracy 0.9168 Iter 6,Testing Accuracy 0.9199 Iter 7,Testing Accuracy 0.9201 Iter 8,Testing Accuracy 0.9202 Iter 9,Testing Accuracy 0.9213 Iter 10,Testing Accuracy 0.921 Iter 11,Testing Accuracy 0.9205 Iter 12,Testing Accuracy 0.9214 Iter 13,Testing Accuracy 0.923 Iter 14,Testing Accuracy 0.9237 Iter 15,Testing Accuracy 0.9238 Iter 16,Testing Accuracy 0.924 Iter 17,Testing Accuracy 0.9231 Iter 18,Testing Accuracy 0.9246 Iter 19,Testing Accuracy 0.925 Iter 20,Testing Accuracy 0.9253