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   源码介绍

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在tensorflow中实现的and和xor函数的例子

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#!/usr/bin/env python

import tensorflow as tf
import math
import numpy as np

INPUT_COUNT = 2
OUTPUT_COUNT = 2
HIDDEN_COUNT = 2
LEARNING_RATE = 0.1
MAX_STEPS = 5000

# For every training loop we are going to provide the same input and expected output data
INPUT_TRAIN = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
OUTPUT_TRAIN = np.array([[1, 0], [0, 1], [0, 1], [1, 0]])

# Nodes are created in Tensorflow using placeholders. Placeholders are values that we will input when we ask Tensorflow to run a computation.
# Create inputs x consisting of a 2d tensor of floating point numbers
inputs_placeholder = tf.placeholder("float",
shape=[None, INPUT_COUNT])
labels_placeholder = tf.placeholder("float",
shape=[None, OUTPUT_COUNT])

# We need to create a python dictionary object with placeholders as keys and feed tensors as values
feed_dict = {
inputs_placeholder: INPUT_TRAIN,
labels_placeholder: OUTPUT_TRAIN,
}

# Define weights and biases from input layer to hidden layer
WEIGHT_HIDDEN = tf.Variable(tf.truncated_normal([INPUT_COUNT, HIDDEN_COUNT]))
BIAS_HIDDEN = tf.Variable(tf.zeros([HIDDEN_COUNT]))

# Define an activation function for the hidden layer. Here we are using the Sigmoid function, but you can use other activation functions offered by Tensorflow.
AF_HIDDEN = tf.nn.sigmoid(tf.matmul(inputs_placeholder, WEIGHT_HIDDEN) BIAS_HIDDEN)

#  Define weights and biases from hidden layer to output layer. The biases are initialized with tf.zeros to make sure they start with zero values.
WEIGHT_OUTPUT = tf.Variable(tf.truncated_normal([HIDDEN_COUNT, OUTPUT_COUNT]))
BIAS_OUTPUT = tf.Variable(tf.zeros([OUTPUT_COUNT]))

# With one line of code we can calculate the logits tensor that will contain the output that is returned
logits = tf.matmul(AF_HIDDEN, WEIGHT_OUTPUT) BIAS_OUTPUT
# We then compute the softmax probabilities that are assigned to each class
y = tf.nn.softmax(logits)

# The tf.nn.softmax_cross_entropy_with_logits op is added to compare the output logits to expected output
#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, y)
cross_entropy = -tf.reduce_sum(labels_placeholder * tf.log(y))
# It then uses tf.reduce_mean to average the cross entropy values across the batch dimension as the total loss
loss = tf.reduce_mean(cross_entropy)

# Next, we instantiate a tf.train.GradientDescentOptimizer that applies gradients with the requested learning rate. Since Tensorflow has access to the entire computation graph, it can find the gradients of the cost of all the variables.
train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(loss)

# Next we create a tf.Session () to run the graph
init = tf.global_variables_initializer()
with tf.Session() as sess:
# Then we run the session
sess.run(init)

# The following code fetch two values [train_step, loss] in its run call. Because there are two values to fetch, sess.run() returns a tuple with two items. We also print the loss and outputs every 100 steps.
for step in range(MAX_STEPS):
loss_val = sess.run([train_step, loss], feed_dict)
if step % 100 == 0:
print ("Step:", step, "loss: ", loss_val)
for input_value in INPUT_TRAIN:
print (input_value, sess.run(y, 
feed_dict={inputs_placeholder: [input_value]}))