import tensorflow as tf from numpy.random import RandomState batch_size = 8 w1 = tf.Variable(tf.random_normal([2,3], stddev = 1, seed = 1)) w2 = tf.Variable(tf.random_normal([3,1], stddev = 1, seed = 1)) x = tf.placeholder(tf.float32, shape = (None, 2), name = 'x-input') y_ = tf.placeholder(tf.float32, shape = (None, 1), name = 'y-input') a = tf.matmul(x, w1) y = tf.matmul(a, w2) y = tf.sigmoid(y) cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)) + (1-y_) * tf.log(tf.clip_by_value(1-y, 1e-10, 1.0))) train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy) rdm = RandomState(1) dataset_size = 128 X = rdm.rand(dataset_size, 2) Y = [[int(x1+x2 < 1)] for (x1,x2) in X] with tf.Session() as sess: init_op = tf.global_variables_initializer() sess.run(init_op) print(sess.run(w1)) print(sess.run(w2)) STEPS = 5000 for i in range(STEPS): start = (i*batch_size) % dataset_size end = min(start+batch_size, dataset_size) sess.run(train_step, feed_dict={x:X[start:end], y_:Y[start:end]}) if i%1000 == 0: total_cross_entropy = sess.run(cross_entropy, feed_dict={x:X, y_:Y}) print("After %d training steps, cross entropy on all data is %g" %(i, total_cross_entropy)) # print("After %d training steps, cross entropy on all data is" %i) # print( total_cross_entropy) print(sess.run(w1)) print(sess.run(w2))
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转载自blog.csdn.net/feidao84/article/details/81990459
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