def get_dropout(layers, num_time_parallel=1):
if self.dropout > 0:
return theano_lstm.MultiDropout([(num_time_parallel, shape)
for shape in layers],
self.dropout)
else:
return []
def setup_train(self):
# dimensions: (batch, time, notes, input_data) with input_data as in architecture
self.input_mat = T.btensor4()
# dimensions: (batch, time, notes, onOrArtic) with 0:on, 1:artic
self.output_mat = T.btensor4()
self.epsilon = np.spacing(np.float32(1.0))
def step_time(in_data, *other):
other = list(other)
split = -len(self.t_layer_sizes) if self.dropout else len(other)
hiddens = other[:split]
masks = [None] + other[split:] if self.dropout else []
new_states = self.time_model.forward(in_data, prev_hiddens=hiddens, dropout=masks)
return new_states
def step_note(in_data, *other):
other = list(other)
split = -len(self.p_layer_sizes) if self.dropout else len(other)
hiddens = other[:split]
masks = [None] + other[split:] if self.dropout else []
new_states = self.pitch_model.forward(in_data, prev_hiddens=hiddens, dropout=masks)
return new_states
# We generate an output for each input, so it doesn't make sense to use the last output as an input.
# Note that we assume the sentinel start value is already present
# TEMP CHANGE: NO SENTINEL
input_slice = self.input_mat[:,0:-1]
n_batch, n_time, n_note, n_ipn = input_slice.shape
# time_inputs is a matrix (time, batch/note, input_per_note)
time_inputs = input_slice.transpose((1,0,2,3)).reshape((n_time,n_batch*n_note,n_ipn))
num_time_parallel = time_inputs.shape[1]
# apply dropout
if self.dropout > 0:
time_masks = theano_lstm.MultiDropout( [(num_time_parallel, shape) for shape in self.t_layer_sizes], self.dropout)
else:
time_masks = []
time_outputs_info = [initial_state_with_taps(layer, num_time_parallel) for layer in self.time_model.layers]
time_result, _ = theano.scan(fn=step_time, sequences=[time_inputs], non_sequences=time_masks, outputs_info=time_outputs_info)
self.time_thoughts = time_result
# Now time_result is a list of matrix [layer](time, batch/note, hidden_states) for each layer but we only care about
# the hidden state of the last layer.
# Transpose to be (note, batch/time, hidden_states)
last_layer = get_last_layer(time_result)
n_hidden = last_layer.shape[2]
time_final = get_last_layer(time_result).reshape((n_time,n_batch,n_note,n_hidden)).transpose((2,1,0,3)).reshape((n_note,n_batch*n_time,n_hidden))
# note_choices_inputs represents the last chosen note. Starts with [0,0], doesn't include last note.
# In (note, batch/time, 2) format
# Shape of start is thus (1, N, 2), concatenated with all but last element of output_mat transformed to (x, N, 2)
start_note_values = T.alloc(np.array(0,dtype=np.int8), 1, time_final.shape[1], 2 )
correct_choices = self.output_mat[:,1:,0:-1,:].transpose((2,0,1,3)).reshape((n_note-1,n_batch*n_time,2))
note_choices_inputs = T.concatenate([start_note_values, correct_choices], axis=0)
# Together, this and the output from the last LSTM goes to the new LSTM, but rotated, so that the batches in
# one direction are the steps in the other, and vice versa.
note_inputs = T.concatenate( [time_final, note_choices_inputs], axis=2 )
num_timebatch = note_inputs.shape[1]
# apply dropout
if self.dropout > 0:
pitch_masks = theano_lstm.MultiDropout( [(num_timebatch, shape) for shape in self.p_layer_sizes], self.dropout)
else:
pitch_masks = []
note_outputs_info = [initial_state_with_taps(layer, num_timebatch) for layer in self.pitch_model.layers]
note_result, _ = theano.scan(fn=step_note, sequences=[note_inputs], non_sequences=pitch_masks, outputs_info=note_outputs_info)
self.note_thoughts = note_result
# Now note_result is a list of matrix [layer](note, batch/time, onOrArticProb) for each layer but we only care about
# the hidden state of the last layer.
# Transpose to be (batch, time, note, onOrArticProb)
note_final = get_last_layer(note_result).reshape((n_note,n_batch,n_time,2)).transpose(1,2,0,3)
# The cost of the entire procedure is the negative log likelihood of the events all happening.
# For the purposes of training, if the ouputted probability is P, then the likelihood of seeing a 1 is P, and
# the likelihood of seeing 0 is (1-P). So the likelihood is (1-P)(1-x) + Px = 2Px - P - x + 1
# Since they are all binary decisions, and are all probabilities given all previous decisions, we can just
# multiply the likelihoods, or, since we are logging them, add the logs.
# Note that we mask out the articulations for those notes that aren't played, because it doesn't matter
# whether or not those are articulated.
# The padright is there because self.output_mat[:,:,:,0] -> 3D matrix with (b,x,y), but we need 3d tensor with
# (b,x,y,1) instead
active_notes = T.shape_padright(self.output_mat[:,1:,:,0])
mask = T.concatenate([T.ones_like(active_notes),active_notes], axis=3)
loglikelihoods = mask * T.log( 2*note_final*self.output_mat[:,1:] - note_final - self.output_mat[:,1:] + 1 + self.epsilon )
self.cost = T.neg(T.sum(loglikelihoods))
updates, _, _, _, _ = create_optimization_updates(self.cost, self.params, method="adadelta")
self.update_fun = theano.function(
inputs=[self.input_mat, self.output_mat],
outputs=self.cost,
updates=updates,
allow_input_downcast=True)
self.update_thought_fun = theano.function(
inputs=[self.input_mat, self.output_mat],
outputs= ensure_list(self.time_thoughts) + ensure_list(self.note_thoughts) + [self.cost],
allow_input_downcast=True)