multicnet.py
def convolve_relu_pool(self, nn_input, weights_shape, stride = 4, pool = True):
        """
        Convolve the input to the network with the weight tensor,
        add the bias, apply the ReLU function and finally max pool
        """
        W_conv, b_conv = self.build_weights_biases(weights_shape)
        h_conv = tf.nn.relu(self.conv2d(nn_input, W_conv, stride) + b_conv)
        if not pool:
            return h_conv
        return self.max_pool_2x2(h_conv)

        Convolve the input to the network with the weight tensor,
        add the bias, apply the ReLU function and finally max pool
        
Convolve the input to the network with the weight tensor,
add the bias, apply the ReLU function and finally max pool
Convolve the input to the network with the weight tensor add the bias apply the ReLU function and finally max pool
def convolve_relu_pool(self, nn_input, weights_shape, stride = 4, pool = True):
        W_conv, b_conv = self.build_weights_biases(weights_shape)
        h_conv = tf.nn.relu(self.conv2d(nn_input, W_conv, stride) + b_conv)
        if not pool:
            return h_conv
        return self.max_pool_2x2(h_conv)
def convolve_relu_pool ( self , nn_input , weights_shape , stride = 4 , pool = True ) : W_conv , b_conv = self . build_weights_biases ( weights_shape ) h_conv = tf . nn . relu ( self . conv2d ( nn_input , W_conv , stride ) + b_conv ) if not pool : return h_conv return self . max_pool_2x2 ( h_conv )
Convolve the input to the network with the weight tensor,
add the bias, apply the ReLU function and finally max pool
Convolve the input to the network with the weight tensor add the bias apply the ReLU function and finally max pool
"""
        Convolve the input to the network with the weight tensor,
        add the bias, apply the ReLU function and finally max pool
        """
self
nn_input
weights_shape
stride
pool