Suppose I want to train a stateful LSTM (going by keras definition, where "stateful ... #model.reset_states() (1) I believe this is 'stateful = False'? ...
If it's not set - all states are automatically reset after every batch computations in your network (so e.g. after calling fit , predict and evaluate also). If ...
26.07.2017 · Regarding the problem of getting quite different results with the two cases when model.reset_states () is removed from the first one: it shouldn't happen. You will get different results from each case if you reset the states of the model between the epochs in one case but not in the other.
reset_states clears only the hidden states of your network. It's worth to mention that depending on if the option stateful=True was set in your network - the behaviour of this function might be different. If it's not set - all states are automatically reset after every batch computations in your network (so e.g. after calling fit, predict and evaluate also).
When restoring a keras model with keras.models.load_model, the returned model's optimizer is in the reset state (e.g. its weights attribute is empty). Describe the expected behavior The original call: reconstructed_model = tf. keras. models. load_model ( "my_model") should have restored and kept the optimizer's weights.
If it's not set - all states are automatically reset after every batch computations in your network (so e.g. after calling fit , predict and evaluate also).
reset_states() to reset the states of all layers in the model; use layer.reset_states() to reset the states of a specific stateful RNN layer. Example: X # this ...
Why do Keras require the batch size in stateful mode? When the model is stateless, Keras allocates an array for the states of size output_dim(understand number of cells in your LSTM). At each sequence processing, this state array is reset. In Stateful model, Keras must propagate the previous states for each sample across the batches.
Answers 90 reset_states clears only the hidden states of your network. It's worth to mention that depending on if the option stateful=True was set in your network - the behaviour of this function might be different.
01.05.2019 · I use tf.keras.Model rather than tf.keras.Sequential so that I can have multiple outputs (i.e. so I can access the hidden state after a forward pass): import numpy as np import tensorflow as tf np . random . seed ( 42 ) tf . random . set_seed ( 42 ) input_dim = 3 output_dim = 3 num_timesteps = 2 batch_size = 10 nodes = 10 input_layer = tf . keras .