def test_rmsprop():
results = []
for scale in scales:
A = cgt.shared(1.0)
B = cgt.shared(1.0)
updates = nn.rmsprop(f(A, scale) + f(B, scale), [A, B], learning_rate=0.01)
do_update = cgt.function([], [], updates=updates)
for _ in range(10):
do_update()
assert np.allclose(A.op.get_value(), B.op.get_value())
results.append(A.op.get_value().copy())
assert np.allclose(results, torch_values['rmsprop'])
def run_rmsprop():
results = []
for scale in scales:
A = cgt.shared(1.0)
B = cgt.shared(1.0)
updates = nn.rmsprop(f(A, scale) + f(B, scale), [A, B], learning_rate=0.01)
do_update = cgt.function([], [], updates=updates)
for _ in range(10):
do_update()
assert np.allclose(A.op.get_value(), B.op.get_value())
results.append(A.op.get_value().copy())
assert np.allclose(results, torch_values['rmsprop'])
def main():
print("Loading data...")
X = cgt.matrix("X", fixed_shape=(None, 28*28))
y = cgt.vector("y", dtype='i8')
model = build_model(X, 0.0)
loss = -cgt.mean(categorical.loglik(y, model))
updates = nn.rmsprop(loss, nn.get_parameters(loss), 0.01)
train = cgt.function(inputs=[X, y], outputs=[], updates=updates)
y_nodrop = cgt.argmax(model, axis=1)
cost_nodrop = -cgt.mean(categorical.loglik(y, model))
err_nodrop = cgt.cast(cgt.not_equal(y_nodrop, y), cgt.floatX).mean()
computeloss = cgt.function(inputs=[X, y], outputs=[err_nodrop, cost_nodrop])
batch_size=128
Xdata, ydata = load_data()
Xtrain = Xdata[0:60000]
ytrain = ydata[0:60000]
Xtest = Xdata[60000:70000]
ytest = ydata[60000:70000]
sortinds = np.random.permutation(60000)
Xtrain = Xtrain[sortinds]
ytrain = ytrain[sortinds]
print fmt_row(10, ["Epoch","Train NLL","Train Err","Test NLL","Test Err","Epoch Time"])
for i_epoch in xrange(3):
tstart = time.time()
for start in xrange(0, Xtrain.shape[0], batch_size):
end = start+batch_size
train(Xtrain[start:end], ytrain[start:end])
elapsed = time.time() - tstart
trainerr, trainloss = computeloss(Xtrain[:len(Xtest)], ytrain[:len(Xtest)])
testerr, testloss = computeloss(Xtest, ytest)
print fmt_row(10, [i_epoch, trainloss, trainerr, testloss, testerr, elapsed])
nnbuilder.save_weights(model, 'mnist')
def main(num_epochs=NUM_EPOCHS):
#cgt.set_precision('half')
print("Building network ...")
# Recurrent layers expect input of shape
# (batch size, max sequence length, number of features)
X = cgt.tensor3(name='X', fixed_shape=(N_BATCH, MAX_LENGTH, 2))
l_forward = nnbuilder.recurrentLayer(nn_input=X, num_units=N_HIDDEN)
l_backward = nnbuilder.recurrentLayer(nn_input=X, num_units=N_HIDDEN, backwards=True)
#l_forward = nnbuilder.LSTMLayer(nn_input=X, num_units=N_HIDDEN, activation=cgt.sigmoid)
#l_backward = nnbuilder.LSTMLayer(nn_input=X, num_units=N_HIDDEN, activation=cgt.sigmoid, backwards=True)
#l_forward = nnbuilder.GRULayer(nn_input=X, num_units=N_HIDDEN, activation=nn.rectify)
#l_backward = nnbuilder.GRULayer(nn_input=X, num_units=N_HIDDEN, activation=nn.rectify, backwards=True)
l_forward_slice = l_forward[:, MAX_LENGTH-1, :] # Take the last element in the forward slice time dimension
l_backward_slice = l_backward[:, 0, :] # And the first element in the backward slice time dimension
l_sum = cgt.concatenate([l_forward_slice, l_backward_slice], axis=1)
l_out = nnbuilder.denseLayer(l_sum, num_units=1, activation=cgt.tanh)
target_values = cgt.vector('target_output')
predicted_values = l_out[:, 0] # For this task we only need the last value
cost = cgt.mean((predicted_values - target_values)**2)
# Compute SGD updates for training
print("Computing updates ...")
updates = nn.rmsprop(cost, nn.get_parameters(l_out), LEARNING_RATE)
#updates = nn.nesterov_momentum(cost, nn.get_parameters(l_out), 0.05)
# cgt functions for training and computing cost
print("Compiling functions ...")
train = cgt.function([X, target_values], cost, updates=updates)
compute_cost = cgt.function([X, target_values], cost)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val = gen_data()
print("Training ...")
time_start = time.time()
try:
for epoch in range(num_epochs):
for _ in range(EPOCH_SIZE):
X, y, m = gen_data()
train(X, y)
cost_val = compute_cost(X_val, y_val)
print("Epoch {} validation cost = {}".format(epoch+1, cost_val))
print ('Epoch took ' + str(time.time() - time_start))
time_start = time.time()
except KeyboardInterrupt:
pass
def test_the_test_problem():
#Works
batch_size = 32 # How many samples do you want to batch.
feat_t_steps = 20 # How many 10ms sound clips.
feat_num_features = 10 # The dimension of the 10ms clips.
max_label_length = feat_t_steps # The maximal label length of the transcription. includes start character.
num_out_classes = 27
num_out_classes_true = num_out_classes + 2
num_batches = 756
num_epochs = 30
feats = cgt.tensor3(fixed_shape=(batch_size, feat_t_steps, feat_num_features))
ground_labels_basis = cgt.tensor3(fixed_shape=(batch_size, max_label_length, num_out_classes_true))
last_time = time.time()
print 'initializing temporal dense layer'
d1 = nnbuilder.temporalDenseLayer(feats, num_units=128, activation=cgt.sigmoid)
#d2 = nnbuilder.temporalDenseLayer(d1, num_units=128, activation=cgt.sigmoid)
d3 = nnbuilder.temporalDenseLayer(d1, num_units=num_out_classes_true, activation=nnbuilder.linear)
out = nn.three_d_softmax(d3, axis=2)
log_probs = None
for iter_step in range(0, max_label_length):
this_character_dist_bc = out[:, iter_step, :]
prev_out_bc = ground_labels_basis[:, iter_step, :]
log_probs_pre = prev_out_bc * this_character_dist_bc
log_probs_pre = cgt.log(cgt.sum(log_probs_pre, axis=1))
if log_probs is None:
log_probs = cgt.sum(log_probs_pre)
else:
log_probs += cgt.sum(log_probs_pre)
log_probs = -log_probs
print 'that took ' + str(time.time() - last_time) + ' seconds'
last_time = time.time()
print 'compiling objective function'
updates = nn.rmsprop(log_probs, nn.get_parameters(log_probs), learning_rate=0.01)
pred_train = cgt.function([feats, ground_labels_basis], [], updates=updates)
pred_fun = cgt.function([feats, ground_labels_basis], [log_probs])
most_likely_chars = cgt.argmax(out, axis=1)
actual_predictions = cgt.function([feats, ground_labels_basis], [most_likely_chars])
print 'that took ' + str(time.time() - last_time) + ' seconds'
test_data = np.load('test_data.npy')
test_labels = np.load('test_labels.npy')
data_mean = np.mean(test_data)
data_sd = np.mean(test_data)
print 'now training'
for one_epoch in range(0, num_epochs):
trained = 0
last_time = time.time()
print 'starting epoch ' + str(one_epoch)
for batch_iter in range(0, num_batches):
batch, labels_basis = normalize_batch_and_labels(test_data, batch_iter, feat_t_steps, data_mean, data_sd,
test_labels, num_out_classes_true)
pred_train(batch, labels_basis)
for batch_iter in range(0, num_batches):
batch, labels_basis = normalize_batch_and_labels(test_data, batch_iter, feat_t_steps, data_mean, data_sd,
test_labels, num_out_classes_true)
trained += pred_fun(batch, labels_basis)[0]
trained = trained/batch_iter
print 'train loss is ' + str(trained)
print 'that took ' + str(time.time() - last_time) + ' seconds'
act_pred = actual_predictions(batch, labels_basis)[0]
print 'an actual prediction is '
print act_pred
def test_seq_2_seq():
batch_size = 32 # How many samples do you want to batch.
feat_t_steps = 3 # How many 10ms sound clips.
feat_num_features = 10 # The dimension of the 10ms clips.
max_label_length = feat_t_steps # The maximal label length of the transcription.
num_out_classes = 27 # 26 letters and space.
num_out_classes_true = 27 + 2 # Start and end tokens are added.
num_batches = 512 # 1032
num_epochs = 40
feats = cgt.tensor3(fixed_shape=(batch_size, feat_t_steps, feat_num_features))
ground_labels_basis = cgt.tensor3(fixed_shape=(batch_size, max_label_length, num_out_classes_true))
last_time = time.time()
print 'initializing seq2seq'
seq2seq = nnbuilder.Seq2Seq(nn_input_btf=feats, num_out_classes=num_out_classes)
print 'that took ' + str(time.time() - last_time) + ' seconds'
last_time = time.time()
print 'making train objective'
train_objective = seq2seq.get_train_objective(max_label_length=max_label_length,
ground_labels_basis_btc=ground_labels_basis)
print 'that took ' + str(time.time() - last_time) + ' seconds'
last_time = time.time()
print 'making updates'
updates = nn.rmsprop(train_objective, nn.get_parameters(train_objective), learning_rate=0.0001)
#updates = nn.nesterov_momentum(train_objective, nn.get_parameters(train_objective), learning_rate=0.0001, mu=0.4)
#updates = nn.momentum(train_objective, nn.get_parameters(train_objective), learning_rate=0.00001, mu=0.4)
#updates = nn.adadelta(train_objective, nn.get_parameters(train_objective), learning_rate=0.0001, rho=0.95)
print 'that took ' + str(time.time() - last_time) + ' seconds'
last_time = time.time()
print 'compiling train function, test function, and prediction output function'
train_function = cgt.function([feats, ground_labels_basis], [], updates=updates)
test_function = cgt.function([feats, ground_labels_basis], [train_objective])
pred = seq2seq.make_prediction(ground_labels_basis_btc=ground_labels_basis, max_label_length=feat_t_steps)
pred_fun = cgt.function([feats, ground_labels_basis], [pred])
print 'that took ' + str(time.time() - last_time) + ' seconds'
test_data = np.load('test_data.npy')
test_labels = np.load('test_labels.npy')
data_mean = np.mean(test_data)
data_sd = np.std(test_data)
print 'now training'
last_time = time.time()
for one_epoch in range(0, num_epochs):
tested = 0
print 'starting epoch ' + str(one_epoch)
for batch_iter in range(0, num_batches):
batch, labels_basis = normalize_batch_and_labels(test_data, batch_iter, feat_t_steps, data_mean, data_sd,
test_labels, num_out_classes_true)
train_function(batch, labels_basis)
for batch_iter in range(0, num_batches):
batch, labels_basis = normalize_batch_and_labels(test_data, batch_iter, feat_t_steps, data_mean, data_sd,
test_labels, num_out_classes_true)
tested += test_function(batch, labels_basis)[0]
tested = tested / batch_iter
print 'train loss for batch ' + str(batch_iter) + ' is ' + str(tested)
print 'an actual prediction is '
print pred_fun(batch, labels_basis)[0]
print 'the truth is'
print test_labels[batch_iter, :, 0:feat_t_steps]
print 'that took ' + str(time.time() - last_time) + ' seconds'
last_time = time.time()
prediction_final = pred_fun(batch, labels_basis)[0]
print prediction_final
