Sorry I cant understand.I'm a student and new to use spyder.
Can you tell me how can I update spyder 4 by Command Prompt or Anaconda
command prompt?
*Have a good day*
*Thank you*

*Bhanupratap*


On Sat, Dec 21, 2019 at 10:57 PM Vivek Katakam <[email protected]> wrote:

> Hi All,
>
> I get getting the following message at the end of output after running my
> mnist dataset program in the Spyder IDE:
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> *Init signature:Dense(    units,    activation=None,    use_bias=True,
> kernel_initializer='glorot_uniform',    bias_initializer='zeros',
> kernel_regularizer=None,    bias_regularizer=None,
> activity_regularizer=None,    kernel_constraint=None,
> bias_constraint=None,    **kwargs,)Docstring:     Just your regular
> densely-connected NN layer.`Dense` implements the operation:`output =
> activation(dot(input, kernel) + bias)`where `activation` is the
> element-wise activation functionpassed as the `activation` argument,
> `kernel` is a weights matrixcreated by the layer, and `bias` is a bias
> vector created by the layer(only applicable if `use_bias` is `True`).Note:
> if the input to the layer has a rank greater than 2, thenit is flattened
> prior to the initial dot product with `kernel`.# Example```python    # as
> first layer in a sequential model:    model = Sequential()
> model.add(Dense(32, input_shape=(16,)))    # now the model will take as
> input arrays of shape (*, 16)    # and output arrays of shape (*, 32)    #
> after the first layer, you don't need to specify    # the size of the input
> anymore:    model.add(Dense(32))```# Arguments    units: Positive integer,
> dimensionality of the output space.    activation: Activation function to
> use        (see [activations](../activations.md)).        If you don't
> specify anything, no activation is applied        (ie. "linear" activation:
> `a(x) = x`).    use_bias: Boolean, whether the layer uses a bias vector.
> kernel_initializer: Initializer for the `kernel` weights matrix        (see
> [initializers](../initializers.md)).    bias_initializer: Initializer for
> the bias vector        (see [initializers](../initializers.md)).
> kernel_regularizer: Regularizer function applied to        the `kernel`
> weights matrix        (see [regularizer](../regularizers.md)).
> bias_regularizer: Regularizer function applied to the bias vector
> (see [regularizer](../regularizers.md)).    activity_regularizer:
> Regularizer function applied to        the output of the layer (its
> "activation").        (see [regularizer](../regularizers.md)).
> kernel_constraint: Constraint function applied to        the `kernel`
> weights matrix        (see [constraints](../constraints.md)).
> bias_constraint: Constraint function applied to the bias vector        (see
> [constraints](../constraints.md)).# Input shape    nD tensor with shape:
> `(batch_size, ..., input_dim)`.    The most common situation would be    a
> 2D input with shape `(batch_size, input_dim)`.# Output shape    nD tensor
> with shape: `(batch_size, ..., units)`.    For instance, for a 2D input
> with shape `(batch_size, input_dim)`,    the output would have shape
> `(batch_size, units)`.File:
> c:\programdata\anaconda3\lib\site-packages\keras\layers\core.pyType:
> typeSubclasses:     *
>
> I did not choose any help topic. even though the above help message is
> comming at the end of output.
> Is this an error in the code or something to do with settings.
>
> Thanks and Regards,
> Vivek
>
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>

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