深度学习作业L4W1（2）：Convolution model - Application - v1

最新推荐文章于 2024-03-29 17:05:55 发布

awake020

最新推荐文章于 2024-03-29 17:05:55 发布

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分类专栏：深度学习笔记文章标签：神经网络卷积 tensorflow 深度学习

本文链接：https://blog.youkuaiyun.com/weixin_44334615/article/details/105912280

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该博客介绍了如何使用TensorFlow构建卷积神经网络，包括参数初始化、前向传播、代价函数的设置，并强调了全连接层不使用激活函数的特点，展现了深度学习框架的便捷性。

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利用tensorflow实现卷积神经网络

placeholder

# GRADED FUNCTION: create_placeholders

def create_placeholders(n_H0, n_W0, n_C0, n_y):
    """
    Creates the placeholders for the tensorflow session.
    
    Arguments:
    n_H0 -- scalar, height of an input image
    n_W0 -- scalar, width of an input image
    n_C0 -- scalar, number of channels of the input
    n_y -- scalar, number of classes
        
    Returns:
    X -- placeholder for the data input, of shape [None, n_H0, n_W0, n_C0] and dtype "float"
    Y -- placeholder for the input labels, of shape [None, n_y] and dtype "float"
    """

    ### START CODE HERE ### (≈2 lines)
    X = tf.placeholder(tf.float32, [None, n_H0, n_W0, n_C0])
    Y = tf.placeholder(tf.float32, [None, n_y])
    ### END CODE HERE ###
    
    return X, Y

跟据输入创建placeholder

参数初始化

# GRADED FUNCTION: initialize_parameters

def initialize_parameters():
    """
    Initializes weight parameters to build a neural network with tensorflow. The shapes are:
                        W1 : [4, 4, 3, 8]
                        W2 : [2, 2, 8, 16]
    Returns:
    parameters -- a dictionary of tensors containing W1, W2
    """
    
    tf.set_random_seed(1)                              # so that your "random" numbers match ours
        
    ### START CODE HERE ### (approx. 2 lines of code)
    W1 = tf.get_variable("W1", [4, 4, 3, 8], initializer = tf.contrib.layers.xavier_initializer(seed = 0))
    W2 = tf.get_variable("W2", [2, 2, 8, 16], initializer = tf.contrib.layers.xavier_initializer(seed = 0))
    ### END CODE HERE ###

    parameters = {
   "W1": W1,
                  "W2": W2}
    
    return parameters