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- import tensorflow as tf
- from numpy.random import RandomState
- # define the size of a batch
- batch_size=4
- # define coefficient matrices
- w1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
- w2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
- # define place for input and output, use param 'None' in shape can make the placeholder more flexible
- x = tf.placeholder(tf.float32, shape=[None, 2], name="x-input")
- y_ = tf.placeholder(tf.float32, shape=[None, 1], name="y-input")
- # forward propagation
- a = tf.matmul(x, w1)
- y = tf.matmul(a, w2)
- # define loss function ( sigmoid : 1/1+exp(-x) ), cross_entropy and train_step
- 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().minimize(cross_entropy)
- # create a simulated dataset with a random number generator
- rdm = RandomState(1)
- dataset_size = 1280
- X = rdm.rand(dataset_size, 2)
- Y = [[int(x1+x2<1)] for (x1, x2) in X]
- with tf.Session() as sess:
- sess.run(tf.global_variables_initializer())
- print w1.eval(session=sess)
- print sess.run(w2)
- # writer = tf.summary.FileWriter("logs", tf.get_default_graph())
- # set the number of iteration
- STEPS = 10000
- 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:
- # calculate cross entropy with some interval
- total_cross_entropy = sess.run(cross_entropy, feed_dict={x: X, y_: Y})
- print ("after %d training step(s), cross entropy on all data is %g." % (i, total_cross_entropy))
- # tf.summary.histogram("iteration-w1", w1)
- # tf.summary.histogram("iteration-w2", w2)
- print sess.run(w1)
- print sess.run(w2)
- # writer.close()
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