""" Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly. """ import tensorflow as tf from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer # load data digits = load_digits() X = digits.data y = digits.target y = LabelBinarizer().fit_transform(y) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3) def add_layer(inputs, in_size, out_size, layer_name, activation_function=None, ): # add one more layer and return the output of this layer Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, ) Wx_plus_b = tf.matmul(inputs, Weights) + biases # here to dropout Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob) if activation_function is None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b, ) return outputs def compute_accuracy(v_xs,v_ys,v_keep_prob): global prediction y_pre = sess.run(prediction,feed_dict={xs:v_xs,keep_prob:v_keep_prob}) correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:v_keep_prob}) return result # define placeholder for inputs to network keep_prob = tf.placeholder(tf.float32) xs = tf.placeholder(tf.float32, [None, 64]) # 8x8 ys = tf.placeholder(tf.float32, [None, 10]) # add output layer l1 = add_layer(xs, 64, 50, 'l1', activation_function=tf.nn.tanh) prediction = add_layer(l1, 50, 10, 'l2', activation_function=tf.nn.softmax) # the loss between prediction and real data cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1])) # loss train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.Session() sess.run(tf.initialize_all_variables()) for i in range(500): # here to determine the keeping probability sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5}) if i % 50 == 0: print(compute_accuracy(X_train, y_train,1),compute_accuracy(X_test, y_test,1))