The dense layer can be defined as a densely-connected common Neural Network layer. The output = activation(dot(input, kernel) +bias) operation is executed by the Dense layer. Here an element-wise activation function is being performed by the activation, so as to pass an activation argument, a matrix of weights called kernel is built by the layer, and bias is a vector created by the layer, which is applicable only if the use_bias is True.
It is to be noted that if the input given to the layer has a rank greater than two, it will be flattened previously to its primary dot product with the kernel.
The input shape layer accepts an nD tensor of shape (batch_size, …, input_dim), and makes sure that its most common situation would have to be a 2D input encompassing a shape of (batch_size, input_dim).
It outputs an nD tensor of shape (batch_size, …, units). For instance, where input is a 2D of shape (batch_size, input_dim), the corresponding output will be of shape (batch_size, units).
This is the layer that implements an activation function on the output.
o activation: Basically, it refers to the name of an activation function to be used, or simply we can say a Theano or TensorFlow operation.
It comprises of an arbitrary input shape. It makes use of an argument called input_shape while using it as an initial layer in the model. The input_shape can be defined as a tuple of integers that does not include the samples axis.
The output shape is the same as that of the input shape.
keras.layers.Dropout(rate, noise_shape=None, seed=None)
The dropout is applied to the input as it prevents overfitting by randomly setting units of a fraction rate to 0 during the training time at each update.
o rate: It refers to a float value between 0 and 1, which represents the fraction units to be dropped.
o noise_shape: It refers to a one-dimension tensor integer that epitomizes the shape of a binary dropout mask, which will be used in its multiplication with the input. If the input shape is (batch_size, timesteps, features), and for all timesteps, you wish your dropout mask to be similar, then, in that case, noise_shape=(batch_size, 1, feature) can be utilized.
o seed: It indicates a python integer that will be used as a random seed.
The flatten layer is used for flattening the input by not affecting the batch size.
o data_format: It can be defined as a string one of channels_last (by default) or channels_first. It is mainly used for ordering the input dimensions, so as to preserve ordering of weight when a model is being switched from one data format to another. Here the channels_last. relates to the input shape of (batch, …, channels), whereas the channels_first relates to the input shape of (batch, channels, …). By default, the image_data_format value found in Keras config file is residing at ~/.keras/keras.json, else if it has not been set, then it will be at "channels_last".
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