def visualize_states(hidden_states, updates, train_stream, valid_stream, args): # Get all the hidden_states filter_states = VariableFilter(theano_name_regex="hidden_state_.*") all_states = filter_states(hidden_states) all_states = sorted(all_states, key=lambda var: var.name[-1]) # Get all the hidden_cells filter_cells = VariableFilter(theano_name_regex="hidden_cells_.*") all_cells = filter_cells(hidden_states) all_cells = sorted(all_cells, key=lambda var: var.name[-1]) # Handle the theano shared variables that allow carrying the hidden state givens, f_updates = carry_hidden_state(updates, 1, not(has_indices(args.dataset))) # Compile the function logger.info("The compilation of the function has started") if args.rnn_type == "lstm" and args.visualize_cells: compiled = theano.function(inputs=ComputationGraph(all_cells).inputs, outputs=all_cells, givens=givens, updates=f_updates, mode=Mode(optimizer='fast_compile')) else: compiled = theano.function(inputs=ComputationGraph(all_states).inputs, outputs=all_states, givens=givens, updates=f_updates, mode=Mode(optimizer='fast_compile')) # Plot the function plot("hidden_state", train_stream, compiled, args)
def visualize_states(hidden_states, updates, train_stream, valid_stream, args): # Get all the hidden_states filter_states = VariableFilter(theano_name_regex="hidden_state_.*") all_states = filter_states(hidden_states) all_states = sorted(all_states, key=lambda var: var.name[-1]) # Get all the hidden_cells filter_cells = VariableFilter(theano_name_regex="hidden_cells_.*") all_cells = filter_cells(hidden_states) all_cells = sorted(all_cells, key=lambda var: var.name[-1]) # Handle the theano shared variables that allow carrying the hidden state givens, f_updates = carry_hidden_state(updates, 1, not (has_indices(args.dataset))) # Compile the function logger.info("The compilation of the function has started") if args.rnn_type == "lstm" and args.visualize_cells: compiled = theano.function(inputs=ComputationGraph(all_cells).inputs, outputs=all_cells, givens=givens, updates=f_updates, mode=Mode(optimizer='fast_compile')) else: compiled = theano.function(inputs=ComputationGraph(all_states).inputs, outputs=all_states, givens=givens, updates=f_updates, mode=Mode(optimizer='fast_compile')) # Plot the function plot("hidden_state", train_stream, compiled, args)
def visualize_gates_soft(gate_values, hidden_states, updates, train_stream, valid_stream, args): # Handle the theano shared variables that allow carrying the hidden state givens, f_updates = carry_hidden_state(updates, 1, not(has_indices(args.dataset))) # Compile the function compiled = theano.function(inputs=ComputationGraph(gate_values).inputs, outputs=gate_values, givens=givens, updates=f_updates, mode=Mode(optimizer='fast_compile')) plot("gates_soft", train_stream, compiled, args)
def visualize_gates_soft(gate_values, hidden_states, updates, train_stream, valid_stream, args): # Handle the theano shared variables that allow carrying the hidden state givens, f_updates = carry_hidden_state(updates, 1, not (has_indices(args.dataset))) # Compile the function compiled = theano.function(inputs=ComputationGraph(gate_values).inputs, outputs=gate_values, givens=givens, updates=f_updates, mode=Mode(optimizer='fast_compile')) plot("gates_soft", train_stream, compiled, args)