TimeDistributedDense applies a same dense to every time step during GRU/LSTM Cell unrolling. So the error function will be between predicted label sequence and the actual label sequence. (Which is normally the requirement for sequence to sequence labeling problems). However, with return_sequences=False, Dense layer is applied only once at the last cell. This is normally … Read more
If you want to get weights and biases of all layers, you can simply use: for layer in model.layers: print(layer.get_config(), layer.get_weights()) This will print all information that’s relevant. If you want the weights directly returned as numpy arrays, you can use: first_layer_weights = model.layers[0].get_weights()[0] first_layer_biases = model.layers[0].get_weights()[1] second_layer_weights = model.layers[1].get_weights()[0] second_layer_biases = model.layers[1].get_weights()[1] etc.
Save the initial weights right after compiling the model but before training it: model.save_weights(‘model.h5’) and then after training, “reset” the model by reloading the initial weights: model.load_weights(‘model.h5’) This gives you an apples to apples model to compare different data sets and should be quicker than recompiling the entire model.
By default, Convolution2D (https://keras.io/layers/convolutional/) expects the input to be in the format (samples, rows, cols, channels), which is “channels-last”. Your data seems to be in the format (samples, channels, rows, cols). You should be able to fix this using the optional keyword data_format=”channels_first” when declaring the Convolution2D layer. model.add(Convolution2D(32, (3, 3), activation=’relu’, input_shape=(1,28,28), data_format=”channels_first”))
Credits to this Github issue comment by Ritchie Ng. # Creating a model from keras.models import Sequential from keras.layers import Dense # Custom activation function from keras.layers import Activation from keras import backend as K from keras.utils.generic_utils import get_custom_objects def custom_activation(x): return (K.sigmoid(x) * 5) – 1 get_custom_objects().update({‘custom_activation’: Activation(custom_activation)}) # Usage model = Sequential() model.add(Dense(32, … Read more
Cheking with some tests, I got to the following conclusion, which is according to the documentation and to Nassim’s answer: First, there isn’t a single state in a layer, but one state per sample in the batch. There are batch_size parallel states in such a layer. Stateful=False In a stateful=False case, all the states are … Read more
Units: The amount of “neurons”, or “cells”, or whatever the layer has inside it. It’s a property of each layer, and yes, it’s related to the output shape (as we will see later). In your picture, except for the input layer, which is conceptually different from other layers, you have: Hidden layer 1: 4 units … Read more
Currently, contrary to what has been stated in documentation, the Dense layer is applied on the last axis of input tensor: Contrary to the documentation, we don’t actually flatten it. It’s applied on the last axis independently. In other words, if a Dense layer with m units is applied on an input tensor of shape … Read more