test_gradient_moment.py 文件源码

def setUp(self):    
    # Set up model
    tf.reset_default_graph()
    X = tf.placeholder(tf.float32, shape=[None, 784])
    y = tf.placeholder(tf.float32, shape=[None, 10])
    W_fc1 = weight_variable([784, 1024])
    b_fc1 = bias_variable([1024])
    h_fc1 = tf.nn.relu(tf.matmul(X, W_fc1) + b_fc1)
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    h_fc2 = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)
    losses = -tf.reduce_sum(y*tf.log(h_fc2), reduction_indices=[1])

    self.loss = tf.reduce_mean(losses)
    self.batch_size = tf.cast(tf.gather(tf.shape(losses), 0), tf.float32)
    self.var_list = [W_fc1, b_fc1, W_fc2, b_fc2]
    self.X = X
    self.y = y

    self.sess = tf.Session()
    self.sess.run(tf.initialize_all_variables())

    self.mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)