logistic_regression_visual.py 文件源码

def add_layers(inputs, in_size, out_size, layer_name, keep_prob, activation_function=None):

    # add one more layer and return the output of this layer
    weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    wx_plus_b = tf.matmul(inputs, weights) + biases

    # here to dropout
    # ? wx_plus_b ?drop?????
    # keep_prob ??????drop?????? sess.run ? feed
    wx_plus_b = tf.nn.dropout(wx_plus_b, keep_prob)

    if activation_function is None:
        outputs = wx_plus_b
    else:
        outputs = activation_function(wx_plus_b)

    tf.histogram_summary(layer_name + '/outputs', outputs)

    return outputs