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Difference between tf.layers.dense and tf.nn.xw_plus_b


Difference between tf.layers.dense and tf.nn.xw_plus_b

By : Kent Weyers
Date : November 22 2020, 04:01 AM
Hope that helps tf.nn.xw_plus_b is a low-level operation that only computes x*W+b and requires existing variables.
tf.layers.dense is a high-level "layer" that creates variables, apply activation can set constrains and apply regularization.
code :
@tf_export('layers.Dense')
class Dense(keras_layers.Dense, base.Layer):
  def call(self, inputs):
    inputs = ops.convert_to_tensor(inputs, dtype=self.dtype)
    rank = common_shapes.rank(inputs)
    if rank > 2:
      # Broadcasting is required for the inputs.
      outputs = standard_ops.tensordot(inputs, self.kernel, [[rank - 1], [0]])
      # Reshape the output back to the original ndim of the input.
      if not context.executing_eagerly():
        shape = inputs.get_shape().as_list()
        output_shape = shape[:-1] + [self.units]
        outputs.set_shape(output_shape)
    else:
      outputs = gen_math_ops.mat_mul(inputs, self.kernel)
    if self.use_bias:
      outputs = nn.bias_add(outputs, self.bias)
    if self.activation is not None:
      return self.activation(outputs)  # pylint: disable=not-callable
    return outputs


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Difference between local and dense layers in CNNs

Difference between local and dense layers in CNNs


By : aprince
Date : March 29 2020, 07:55 AM
Hope that helps Quoting from cuda-convnet:
Are tf.layers.dense() and tf.contrib.layers.fully_connected() interchangeable?

Are tf.layers.dense() and tf.contrib.layers.fully_connected() interchangeable?


By : eatnotme
Date : March 29 2020, 07:55 AM
wish helps you They are essentially the same, the later calling the former.
However tf.contrib.fully_connected adds a few functionalities on top of dense, in particular the possibility to pass a normalization and an activation in the parameters, à la Keras. As noted by @wordforthewise, mind that the later defaults to tf.nn.relu.
TensorFlow 2.0 How to get trainable variables from tf.keras.layers layers, like Conv2D or Dense

TensorFlow 2.0 How to get trainable variables from tf.keras.layers layers, like Conv2D or Dense


By : user3484727
Date : March 29 2020, 07:55 AM
hope this fix your issue Ok, so I think I found the problem.
The trainable variables were not available until I used the given layer object. After I run my forward pass I could retrieve attributes of the tf.keras.layers.Layer object like trainable_variables and weights.
code :
with tf.GradientTape() as tape:
    print(dense_layers[0].trainable_variables)
    self.forward_pass(X)
    self.compute_loss()
    print(dense_layers[0].trainable_variables)
What is the default kernel initializer in tf.layers.conv2d and tf.layers.dense?

What is the default kernel initializer in tf.layers.conv2d and tf.layers.dense?


By : nilsfrahm
Date : March 29 2020, 07:55 AM
Hope this helps The official Tensorflow API doc claims that the parameter kernel_initializer defaults to None for tf.layers.conv2d and tf.layers.dense. ,
Great question! It is quite a trick to find out!
What is Difference Between Flatten() and Dense() Layers in Convolutional Neural Network?

What is Difference Between Flatten() and Dense() Layers in Convolutional Neural Network?


By : Benny Jonsson
Date : October 13 2020, 11:00 AM
will help you Flatten as the name implies, converts your multidimensional matrices (Batch.Size x Img.W x Img.H x Kernel.Size) to a nice single 2-dimensional matrix: (Batch.Size x (Img.W x Img.H x Kernel.Size)). During backpropagation it also converts back your delta of size (Batch.Size x (Img.W x Img.H x Kernel.Size)) to the original (Batch.Size x Img.W x Img.H x Kernel.Size).
Dense layer is of course the standard fully connected layer.
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