More layer and unit details

[tonio73]

Missing layer information in the viewer:

• loss (training)
• activation at output (output)
• regularization (L1/L2, kernel/bias/activity) (training)
• dropout at input (training)
• batch normalization at input (input)
• Max/Average pooling at output (output)

Missing unit information in the viewer:

• bias/intercept (.use_bias, .bias) (training)
• input shape (.input.get_shape()) (structure)
• output shape (.ouput_shape[1:]) (structure), take into account for pooling and flatten
• padding (Convo) (.padding) (structure)

Later:

• Layer metrics

Implementation:

• foreach layer and unit create a decorator dictionary with following attributes containing lists of strings
• attributes : input, structure, output, training
• fill this structure in bridge.tensorflow.keras_extract_sequential_network() taking into account for the fact that the layer may not exist (previous layers like dropout, batchnorm) or may already exist (layers at output like pooling, activation)

[tonio73]

Depends on #22