tensorflow_tutorials/deep_mnist/deep_mnist.py
def convolutional_layer(cls, weight_shape, bias_shape, inputs):
        """Return a new convolutional layer."""
        weights = cls.weight_variable(weight_shape)
        bias = cls.bias_variable(bias_shape)

        convolution = tf.nn.relu(cls.conv2d(inputs, weights) + bias)

        return cls.max_pool_2x2(convolution)
Return a new convolutional layer.
Return a new convolutional layer.
Return a new convolutional layer .
def convolutional_layer(cls, weight_shape, bias_shape, inputs):
        weights = cls.weight_variable(weight_shape)
        bias = cls.bias_variable(bias_shape)
        convolution = tf.nn.relu(cls.conv2d(inputs, weights) + bias)
        return cls.max_pool_2x2(convolution)
def convolutional_layer ( cls , weight_shape , bias_shape , inputs ) : weights = cls . weight_variable ( weight_shape ) bias = cls . bias_variable ( bias_shape ) convolution = tf . nn . relu ( cls . conv2d ( inputs , weights ) + bias ) return cls . max_pool_2x2 ( convolution )
Return a new convolutional layer.
Return a new convolutional layer .
"""Return a new convolutional layer."""
cls
weight_shape
bias_shape
inputs