forward propagation
时间: 2023-04-29 13:02:22 浏览: 48
正向传播(forward propagation)是神经网络中的一种计算方法,它将输入数据从输入层传递到输出层,计算出网络的输出结果。在正向传播过程中,每个神经元都会对前一层的输出进行加权和计算,并通过激活函数进行非线性变换,最终输出到下一层。正向传播是神经网络中的基本计算过程,也是训练神经网络的第一步。
相关问题
# GRADED FUNCTION: forward_propagation def forward_propagation(X, parameters): """ Implements the forward propagation for the model: CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED Arguments: X -- input dataset placeholder, of shape (input size, number of examples) parameters -- python dictionary containing your parameters "W1", "W2" the shapes are given in initialize_parameters Returns: Z3 -- the output of the last LINEAR unit """ # Retrieve the parameters from the dictionary "parameters" W1 = parameters['W1'] W2 = parameters['W2'] ### START CODE HERE ### # CONV2D: stride of 1, padding 'SAME' Z1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME') # RELU A1 = tf.nn.relu(Z1) # MAXPOOL: window 8x8, sride 8, padding 'SAME' P1 = tf.nn.max_pool(A1, ksize=[1, 8, 8, 1], strides=[1, 8, 8, 1], padding='SAME') # CONV2D: filters W2, stride 1, padding 'SAME' Z2 = tf.nn.conv2d(P1, W2, strides=[1, 1, 1, 1], padding='SAME') # RELU A2 = tf.nn.relu(Z2) # MAXPOOL: window 4x4, stride 4, padding 'SAME' P2 = tf.nn.max_pool(A2, ksize=[1, 4, 4, 1], strides=[1, 4, 4, 1], padding='SAME') # FLATTEN P2 = tf.contrib.layers.flatten(P2) # FULLY-CONNECTED without non-linear activation function (not not call softmax). # 6 neurons in output layer. Hint: one of the arguments should be "activation_fn=None" Z3 = tf.contrib.layers.fully_connected(P2, 6, activation_fn=None) ### END CODE HERE ### return Z3 tf.reset_default_graph() with tf.Session() as sess: np.random.seed(1) X, Y = create_placeholders(64, 64, 3, 6) parameters = initialize_parameters() Z3 = forward_propagation(X, parameters) init = tf.global_variables_initializer() sess.run(init) a = sess.run(Z3, {X: np.random.randn(2,64,64,3), Y: np.random.randn(2,6)}) print("Z3 = " + str(a)) 请根据现在python版本修改这段代码
这段代码是基于TensorFlow 1.x版本的,如果你使用的是TensorFlow 2.x版本,需要对代码进行一些修改。下面是修改后的代码:
```python
import tensorflow as tf
def forward_propagation(X, parameters):
"""
Implements the forward propagation for the model:
CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
Arguments:
X -- input dataset placeholder, of shape (input size, number of examples)
parameters -- python dictionary containing your parameters "W1", "W2"
the shapes are given in initialize_parameters
Returns:
Z3 -- the output of the last LINEAR unit
"""
# Retrieve the parameters from the dictionary "parameters"
W1 = parameters['W1']
W2 = parameters['W2']
# CONV2D: stride of 1, padding 'SAME'
Z1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME')
# RELU
A1 = tf.nn.relu(Z1)
# MAXPOOL: window 8x8, sride 8, padding 'SAME'
P1 = tf.nn.max_pool2d(A1, ksize=[1, 8, 8, 1], strides=[1, 8, 8, 1], padding='SAME')
# CONV2D: filters W2, stride 1, padding 'SAME'
Z2 = tf.nn.conv2d(P1, W2, strides=[1, 1, 1, 1], padding='SAME')
# RELU
A2 = tf.nn.relu(Z2)
# MAXPOOL: window 4x4, stride 4, padding 'SAME'
P2 = tf.nn.max_pool2d(A2, ksize=[1, 4, 4, 1], strides=[1, 4, 4, 1], padding='SAME')
# FLATTEN
P2 = tf.reshape(P2, [-1, P2.shape[1] * P2.shape[2] * P2.shape[3]])
# FULLY-CONNECTED without non-linear activation function (not not call softmax).
# 6 neurons in output layer. Hint: one of the arguments should be "activation_fn=None"
Z3 = tf.contrib.layers.fully_connected(P2, 6, activation_fn=None)
return Z3
tf.random.set_seed(1)
with tf.compat.v1.Session() as sess:
tf.compat.v1.reset_default_graph()
X, Y = create_placeholders(64, 64, 3, 6)
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
init = tf.compat.v1.global_variables_initializer()
sess.run(init)
a = sess.run(Z3, {X: np.random.randn(2,64,64,3), Y: np.random.randn(2,6)})
print("Z3 = " + str(a))
```
这段代码已经修改为兼容TensorFlow 2.x版本,并进行了相应的调整。请注意,TensorFlow 2.x版本中的一些函数名称和用法可能与1.x版本有所不同。
什么是forward
"forward"通常用作动词,表示向前移动、传送或发送。在不同的上下文中,它可以有不同的含义。
在计算机科学中,"forward"可以指数据或信号的传递。例如,在神经网络中,数据在输入层通过隐藏层最终传递到输出层的过程被称为"forward propagation"(前向传播)。
在电子邮件或消息传递中,"forward"指的是将接收到的邮件或消息转发给其他人。
在体育比赛中,例如足球或篮球,"forward"是指将球向前传递给队友,以推动进攻。
总的来说,"forward"表示向前移动、传送或发送某些东西。具体的含义取决于上下文。