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)
