def main_theano_sign_lang_var_len_adadelta():
"""
:description: this trains a model on the sign language data as well, but accounts for variable length sequences and processes batches.
"""
print('loading data...')
n_input_at_each_timestep = 10
n_classes = 97 # no base 0 considered, there are just 98 of them. May need to be 97
X, y = sign_lang.load_data_from_aggregate_file()
X, masks = sign_lang.pad_data_to_max_sample_length(X)
X = X.astype(theano.config.floatX)
masks = masks.astype(theano.config.floatX)
X = np.swapaxes(X, 0, 1)
masks = np.swapaxes(masks, 0, 1)
split_idx = int(.8 * X.shape[1])
X = theano.shared(np.asarray(X, dtype=theano.config.floatX), borrow=True)
masks = theano.shared(np.asarray(masks, dtype=theano.config.floatX),
borrow=True)
y = theano.shared(y, borrow=True)
trainset_masks = masks[:, :split_idx, :]
testset_masks = masks[:, split_idx:, :]
trainset_X, trainset_y = X[:, :split_idx, :], y[:split_idx]
testset_X, testset_y = X[:, split_idx:, :], y[split_idx:]
index = T.lscalar()
x = T.tensor3('x')
target = T.lvector('target')
print_x = theano.printing.Print('\nx')(x)
print_target = theano.printing.Print('target')(target)
mask = T.tensor3('mask')
print('building model...')
lstm_1_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/lstm_1.save'
lstm_2_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/lstm_2.save'
lstm_3_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/lstm_3.save'
softmax_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/softmax_1.save'
lstm_1 = load_model(lstm_1_filepath)
# lstm_2 = load_model(lstm_2_filepath)
# lstm_3 = load_model(lstm_3_filepath)
softmax = load_model(softmax_filepath)
#lstm_1 = variable_length_sequence_lstm.LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='rec_1', return_indices=[-1], dropout_prob=0.3)
#lstm_2 = variable_length_sequence_lstm.LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='rec_2', return_indices=None, dropout_prob=0.3)
#lstm_3 = variable_length_sequence_lstm.LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='rec_3', return_indices=[-1], dropout_prob=0.3)
#softmax = variable_length_sequence_lstm.Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)
# layers = [lstm_1, lstm_2, lstm_3, softmax]
layers = [lstm_1, softmax]
cost_expr = variable_length_sequence_lstm.Softmax.negative_log_likelihood
rnn = variable_length_sequence_lstm.MLP(layers,
cost=cost_expr,
return_indices=[-1])
cost, updates = rnn.get_cost_updates(x, target, mask, learning_rate=0.005)
batch_size = 10
print('building trainer...')
trainer = theano.function(
[index], [cost],
updates=updates,
givens={
x: trainset_X[:, index * batch_size:(index + 1) * batch_size],
target: trainset_y[index * batch_size:(index + 1) * batch_size],
mask: trainset_masks[:,
index * batch_size:(index + 1) * batch_size]
},
mode='FAST_RUN')
errors = rnn.layers[-1].errors(target)
validate_model = theano.function(
inputs=[index],
outputs=[cost, errors],
givens={
x: testset_X[:, index * batch_size:(index + 1) * batch_size],
target: testset_y[index * batch_size:(index + 1) * batch_size],
mask: testset_masks[:, index * batch_size:(index + 1) * batch_size]
},
mode='FAST_RUN')
print('training model...')
n_train_examples = trainset_X.shape.eval()[1]
n_test_examples = testset_X.shape.eval()[1]
n_epochs = 1000
lowest_cost = -1
n_train_batches = int(trainset_X.shape.eval()[1] / float(batch_size))
n_validation_batches = int(testset_X.shape.eval()[1] / float(batch_size))
for epoch in range(n_epochs):
costs = []
#random_indices = get_random_indices(max_index=n_train_examples - 1, samples_per_epoch=100)
for sample_idx in range(n_train_batches):
# for sample_idx in random_indices:
costs.append(trainer(sample_idx)[0])
avg_cost = np.mean(costs)
print('training cost for epoch {0}: {1}'.format(epoch, avg_cost))
if lowest_cost == -1 or avg_cost < lowest_cost * 0.99:
lowest_cost = avg_cost
run_validation = True
save_model(lstm_1, lstm_1_filepath)
# save_model(lstm_2, lstm_2_filepath)
# save_model(lstm_3, lstm_3_filepath)
save_model(softmax, softmax_filepath)
predictions = []
if run_validation:
print('\nvalidation')
for sample_idx in range(n_validation_batches):
predictions.append(validate_model(sample_idx)[1])
accuracy = (1 - np.mean(predictions)) * 100
print('accuracy for epoch {0}: {1}%'.format(epoch, accuracy))
run_validation = False
# print('finished training, final stats:\nfinal cost: {0}\naccuracy: {1}%'.format(np.mean(costs), accuracy))
print('finished training, final stats:\nfinal cost: {0}'.format(
np.mean(costs)))
for layer in rnn.layers:
for param in layer.params:
print('{}: {}'.format(param.name, param.get_value()))
def main_theano_sign_lang_var_len_adadelta():
"""
:description: this trains a model on the sign language data as well, but accounts for variable length sequences and processes batches.
"""
print('loading data...')
n_input_at_each_timestep = 10
n_classes = 97 # no base 0 considered, there are just 98 of them. May need to be 97
X, y = sign_lang.load_data_from_aggregate_file()
X, masks = sign_lang.pad_data_to_max_sample_length(X)
X = X.astype(theano.config.floatX)
masks = masks.astype(theano.config.floatX)
X = np.swapaxes(X, 0, 1)
masks = np.swapaxes(masks, 0, 1)
split_idx = int(.8 * X.shape[1])
X = theano.shared(np.asarray(X, dtype=theano.config.floatX), borrow=True)
masks = theano.shared(np.asarray(masks, dtype=theano.config.floatX), borrow=True)
y = theano.shared(y, borrow=True)
trainset_masks = masks[:, :split_idx, :]
testset_masks = masks[:, split_idx:, :]
trainset_X, trainset_y = X[:, :split_idx, :], y[:split_idx]
testset_X, testset_y = X[:, split_idx:, :], y[split_idx:]
index = T.lscalar()
x = T.tensor3('x')
target = T.lvector('target')
print_x = theano.printing.Print('\nx')(x)
print_target = theano.printing.Print('target')(target)
mask = T.tensor3('mask')
print('building model...')
lstm_1_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/lstm_1.save'
lstm_2_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/lstm_2.save'
lstm_3_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/lstm_3.save'
softmax_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/softmax_1.save'
lstm_1 = load_model(lstm_1_filepath)
# lstm_2 = load_model(lstm_2_filepath)
# lstm_3 = load_model(lstm_3_filepath)
softmax = load_model(softmax_filepath)
#lstm_1 = variable_length_sequence_lstm.LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='rec_1', return_indices=[-1], dropout_prob=0.3)
#lstm_2 = variable_length_sequence_lstm.LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='rec_2', return_indices=None, dropout_prob=0.3)
#lstm_3 = variable_length_sequence_lstm.LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='rec_3', return_indices=[-1], dropout_prob=0.3)
#softmax = variable_length_sequence_lstm.Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)
# layers = [lstm_1, lstm_2, lstm_3, softmax]
layers = [lstm_1, softmax]
cost_expr = variable_length_sequence_lstm.Softmax.negative_log_likelihood
rnn = variable_length_sequence_lstm.MLP(layers, cost=cost_expr, return_indices=[-1])
cost, updates = rnn.get_cost_updates(x, target, mask, learning_rate=0.005)
batch_size = 10
print('building trainer...')
trainer = theano.function(
[index],
[cost],
updates=updates,
givens={
x: trainset_X[:, index * batch_size: (index + 1) * batch_size],
target: trainset_y[index * batch_size: (index + 1) * batch_size],
mask: trainset_masks[:, index * batch_size: (index + 1) * batch_size]
},
mode='FAST_RUN'
)
errors = rnn.layers[-1].errors(target)
validate_model = theano.function(
inputs=[index],
outputs=[cost, errors],
givens={
x: testset_X[:, index * batch_size: (index + 1) * batch_size],
target: testset_y[index * batch_size: (index + 1) * batch_size],
mask: testset_masks[:, index * batch_size: (index + 1) * batch_size]
},
mode='FAST_RUN'
)
print('training model...')
n_train_examples = trainset_X.shape.eval()[1]
n_test_examples = testset_X.shape.eval()[1]
n_epochs = 1000
lowest_cost = -1
n_train_batches = int(trainset_X.shape.eval()[1] / float(batch_size))
n_validation_batches = int(testset_X.shape.eval()[1] / float(batch_size))
for epoch in range(n_epochs):
costs = []
#random_indices = get_random_indices(max_index=n_train_examples - 1, samples_per_epoch=100)
for sample_idx in range(n_train_batches):
# for sample_idx in random_indices:
costs.append(trainer(sample_idx)[0])
avg_cost = np.mean(costs)
print('training cost for epoch {0}: {1}'.format(epoch, avg_cost))
if lowest_cost == -1 or avg_cost < lowest_cost * 0.99:
lowest_cost = avg_cost
run_validation = True
save_model(lstm_1, lstm_1_filepath)
# save_model(lstm_2, lstm_2_filepath)
# save_model(lstm_3, lstm_3_filepath)
save_model(softmax, softmax_filepath)
predictions = []
if run_validation:
print('\nvalidation')
for sample_idx in range(n_validation_batches):
predictions.append(validate_model(sample_idx)[1])
accuracy = (1 - np.mean(predictions)) * 100
print('accuracy for epoch {0}: {1}%'.format(epoch, accuracy))
run_validation = False
# print('finished training, final stats:\nfinal cost: {0}\naccuracy: {1}%'.format(np.mean(costs), accuracy))
print('finished training, final stats:\nfinal cost: {0}'.format(np.mean(costs)))
for layer in rnn.layers:
for param in layer.params:
print('{}: {}'.format(param.name, param.get_value()))
def main_theano_sign_lang():
print('loading data...')
n_input_at_each_timestep = 10
n_classes = 97 # no base 0 considered, there are just 98 of them. May need to be 97
dataset_sequence_length = 31
X, y = sign_lang.load_data_from_aggregate_file()
X = chest_accel.truncate_to_smallest(X)
split_idx = int(.8 * X.shape[0])
X = theano.shared(np.asarray(X, dtype=theano.config.floatX), borrow=True)
y = theano.shared(y, borrow=True)
trainset_X, trainset_y = X[:split_idx], y[:split_idx]
testset_X, testset_y = X[split_idx:], y[split_idx:]
index = T.lscalar()
x = T.matrix('x')
target = T.lscalar('target')
print_x = theano.printing.Print('\nx')(x)
print_target = theano.printing.Print('target')(target)
print('building model...')
# layers = [EncDecRecurrent(n_vis=n_input_at_each_timestep, n_hid=rec_n_hid, return_indices=[-1]), Softmax(n_vis=rec_n_hid, n_classes=n_classes)]
# single layer
#layers = [EncDecRecurrent(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='recurrent', return_indices=[-1]), Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
# double layer
#layers = [EncDecRecurrent(n_vis=n_input_at_each_timestep, n_hid=rec_n_hid, layer_name='rec_1'), EncDecRecurrent(n_vis=rec_n_hid, n_hid=n_input_at_each_timestep, layer_name='rec_2',return_indices=[-1]), Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
# lstm
#layers = [LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm', return_indices=[-1], dropout_prob=0.3), Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
# 2*lstm
#layers = [LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_1', dropout_prob=0.2),LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_2', dropout_prob=0.2, return_indices=[-1]), Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
encoding_rec_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/enc_dec_overlap_1.save'
lstm_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/lstm_1.save'
softmax_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/softmax_1.save'
encoding_rec = load_model(encoding_rec_filepath)
# recurrent_1 = load_model(lstm_filepath)
# softmax = load_model(softmax_filepath)
# encoding_rec = encoding_recurrent_overlap.EncodingRecurrentOverlap(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='enc_1')
# print('building pretrainer...')
# pre_cost, pre_updates = encoding_rec.get_pretraining_cost_updates(x, learning_rate=0.001)
# pretrainer = theano.function(
# [index],
# [pre_cost],
# updates=pre_updates,
# givens={
# x: trainset_X[index]
# },
# mode='FAST_RUN'
# )
# print('pretraining model...')
# n_epochs = 20
# n_train_examples = trainset_X.shape.eval()[0]
# for epoch in range(n_epochs):
# costs = []
# #random_indices = get_random_indices(max_index=n_train_examples - 1, samples_per_epoch=10)
# for sample_idx in range(n_train_examples):
# #for sample_idx in random_indices:
# costs.append(pretrainer(sample_idx)[0])
# print('training cost for epoch {0}: {1}'.format(epoch, np.mean(costs)))
# for param in encoding_rec.reconstruction_params:
# print('{}: {}'.format(param.name, param.get_value()))
# save_model(encoding_rec, encoding_rec_filepath)
recurrent_1 = LSTM(n_vis=n_input_at_each_timestep,
n_hid=n_input_at_each_timestep,
layer_name='rec_1',
return_indices=[-1],
dropout_prob=0.3)
# #recurrent_2 = LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='rec_2', return_indices=[-1], dropout_prob=0.2)
softmax = Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)
# 1*encoding + recurrent
layers = [encoding_rec, recurrent_1, softmax]
# layers = [recurrent_1, softmax]
# 3*lstm
# layers = [LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_1'),
# LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_2'),
# LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_3', return_indices=[-1]),
# Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
# rnn = EncDecRNN(layers, cost=model_cost, return_indices=[-1])
cost = Softmax.negative_log_likelihood
rnn = EncDecRNN(layers, cost=cost, return_indices=[-1])
# cost, updates = rnn.get_cost_updates((x, print_target))
cost, updates = rnn.get_cost_updates((x, target))
print('building trainer...')
trainer = theano.function([index], [cost],
updates=updates,
givens={
x: trainset_X[index],
target: trainset_y[index]
},
mode='FAST_RUN')
errors = rnn.layers[-1].errors(target)
validate_model = theano.function(inputs=[index],
outputs=[cost, errors],
givens={
x: testset_X[index],
target: testset_y[index]
},
mode='FAST_RUN')
print('training model...')
n_train_examples = trainset_X.shape.eval()[0]
n_test_examples = testset_X.shape.eval()[0]
n_epochs = 100
lowest_cost = -1
for epoch in range(n_epochs):
costs = []
#random_indices = get_random_indices(max_index=n_train_examples - 1, samples_per_epoch=100)
for sample_idx in range(n_train_examples):
# for sample_idx in random_indices:
costs.append(trainer(sample_idx)[0])
avg_cost = np.mean(costs)
print('training cost for epoch {0}: {1}'.format(epoch, avg_cost))
if lowest_cost == -1 or avg_cost < lowest_cost * 0.98:
lowest_cost = avg_cost
run_validation = True
save_model(recurrent_1, lstm_filepath)
save_model(softmax, softmax_filepath)
predictions = []
if run_validation:
print('\nvalidation')
for sample_idx in range(n_test_examples):
predictions.append(validate_model(sample_idx)[1])
accuracy = (1 - np.mean(predictions)) * 100
print('accuracy for epoch {0}: {1}%'.format(epoch, accuracy))
run_validation = False
# print('finished training, final stats:\nfinal cost: {0}\naccuracy: {1}%'.format(np.mean(costs), accuracy))
print('finished training, final stats:\nfinal cost: {0}'.format(
np.mean(costs)))
for layer in rnn.layers:
for param in layer.params:
print('{}: {}'.format(param.name, param.get_value()))
def main_theano_sign_lang():
print('loading data...')
n_input_at_each_timestep = 10
n_classes = 97 # no base 0 considered, there are just 98 of them. May need to be 97
dataset_sequence_length = 31
X, y = sign_lang.load_data_from_aggregate_file()
X = chest_accel.truncate_to_smallest(X)
split_idx = int(.8 * X.shape[0])
X = theano.shared(np.asarray(X, dtype=theano.config.floatX), borrow=True)
y = theano.shared(y, borrow=True)
trainset_X, trainset_y = X[:split_idx], y[:split_idx]
testset_X, testset_y = X[split_idx:], y[split_idx:]
index = T.lscalar()
x = T.matrix('x')
target = T.lscalar('target')
print_x = theano.printing.Print('\nx')(x)
print_target = theano.printing.Print('target')(target)
print('building model...')
# layers = [EncDecRecurrent(n_vis=n_input_at_each_timestep, n_hid=rec_n_hid, return_indices=[-1]), Softmax(n_vis=rec_n_hid, n_classes=n_classes)]
# single layer
#layers = [EncDecRecurrent(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='recurrent', return_indices=[-1]), Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
# double layer
#layers = [EncDecRecurrent(n_vis=n_input_at_each_timestep, n_hid=rec_n_hid, layer_name='rec_1'), EncDecRecurrent(n_vis=rec_n_hid, n_hid=n_input_at_each_timestep, layer_name='rec_2',return_indices=[-1]), Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
# lstm
#layers = [LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm', return_indices=[-1], dropout_prob=0.3), Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
# 2*lstm
#layers = [LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_1', dropout_prob=0.2),LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_2', dropout_prob=0.2, return_indices=[-1]), Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
encoding_rec_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/enc_dec_overlap_1.save'
lstm_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/lstm_1.save'
softmax_filepath = '/Users/wulfe/Dropbox/Start/scripts/machine_learning/stacked_enc_dec_rnn/models/softmax_1.save'
encoding_rec = load_model(encoding_rec_filepath)
# recurrent_1 = load_model(lstm_filepath)
# softmax = load_model(softmax_filepath)
# encoding_rec = encoding_recurrent_overlap.EncodingRecurrentOverlap(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='enc_1')
# print('building pretrainer...')
# pre_cost, pre_updates = encoding_rec.get_pretraining_cost_updates(x, learning_rate=0.001)
# pretrainer = theano.function(
# [index],
# [pre_cost],
# updates=pre_updates,
# givens={
# x: trainset_X[index]
# },
# mode='FAST_RUN'
# )
# print('pretraining model...')
# n_epochs = 20
# n_train_examples = trainset_X.shape.eval()[0]
# for epoch in range(n_epochs):
# costs = []
# #random_indices = get_random_indices(max_index=n_train_examples - 1, samples_per_epoch=10)
# for sample_idx in range(n_train_examples):
# #for sample_idx in random_indices:
# costs.append(pretrainer(sample_idx)[0])
# print('training cost for epoch {0}: {1}'.format(epoch, np.mean(costs)))
# for param in encoding_rec.reconstruction_params:
# print('{}: {}'.format(param.name, param.get_value()))
# save_model(encoding_rec, encoding_rec_filepath)
recurrent_1 = LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='rec_1', return_indices=[-1], dropout_prob=0.3)
# #recurrent_2 = LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='rec_2', return_indices=[-1], dropout_prob=0.2)
softmax = Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)
# 1*encoding + recurrent
layers = [encoding_rec, recurrent_1, softmax]
# layers = [recurrent_1, softmax]
# 3*lstm
# layers = [LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_1'),
# LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_2'),
# LSTM(n_vis=n_input_at_each_timestep, n_hid=n_input_at_each_timestep, layer_name='lstm_3', return_indices=[-1]),
# Softmax(n_vis=n_input_at_each_timestep, n_classes=n_classes)]
# rnn = EncDecRNN(layers, cost=model_cost, return_indices=[-1])
cost = Softmax.negative_log_likelihood
rnn = EncDecRNN(layers, cost=cost, return_indices=[-1])
# cost, updates = rnn.get_cost_updates((x, print_target))
cost, updates = rnn.get_cost_updates((x, target))
print('building trainer...')
trainer = theano.function(
[index],
[cost],
updates=updates,
givens={
x: trainset_X[index],
target: trainset_y[index]
},
mode='FAST_RUN'
)
errors = rnn.layers[-1].errors(target)
validate_model = theano.function(
inputs=[index],
outputs=[cost, errors],
givens={
x: testset_X[index],
target: testset_y[index]
},
mode='FAST_RUN'
)
print('training model...')
n_train_examples = trainset_X.shape.eval()[0]
n_test_examples = testset_X.shape.eval()[0]
n_epochs = 100
lowest_cost = -1
for epoch in range(n_epochs):
costs = []
#random_indices = get_random_indices(max_index=n_train_examples - 1, samples_per_epoch=100)
for sample_idx in range(n_train_examples):
# for sample_idx in random_indices:
costs.append(trainer(sample_idx)[0])
avg_cost = np.mean(costs)
print('training cost for epoch {0}: {1}'.format(epoch, avg_cost))
if lowest_cost == -1 or avg_cost < lowest_cost * 0.98:
lowest_cost = avg_cost
run_validation = True
save_model(recurrent_1, lstm_filepath)
save_model(softmax, softmax_filepath)
predictions = []
if run_validation:
print('\nvalidation')
for sample_idx in range(n_test_examples):
predictions.append(validate_model(sample_idx)[1])
accuracy = (1 - np.mean(predictions)) * 100
print('accuracy for epoch {0}: {1}%'.format(epoch, accuracy))
run_validation = False
# print('finished training, final stats:\nfinal cost: {0}\naccuracy: {1}%'.format(np.mean(costs), accuracy))
print('finished training, final stats:\nfinal cost: {0}'.format(np.mean(costs)))
for layer in rnn.layers:
for param in layer.params:
print('{}: {}'.format(param.name, param.get_value()))
