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| 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_go = tf.global_variables_initializer() sess.run(init_go)
print('parameter w1 before train: ', sess.run(w1)) print('parameter w2 before train: ', 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('parameter w1 after train: ', sess.run(w1)) print('parameter w2 after train: ', sess.run(w2))
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