) # the data is presented as a vector of inputs with many exchangeable examples of this vector
is_train = T.iscalar(
'is_train') # pseudo boolean for switching between training and prediction
rng = numpy.random.RandomState(1234)
# Architecture: input --> LSTM --> predict one-ahead
lstm_1 = LSTM(rng,
x,
n_in=data_set_x.get_value(borrow=True).shape[1],
n_out=n_hidden)
output = LogisticRegression(input=lstm_1.output,
n_in=n_hidden,
n_out=data_set_x.get_value(borrow=True).shape[1])
################################
# Objective function and GD
################################
print 'defining cost, parameters, and learning function...'
# the cost we minimize during training is the negative log likelihood of
# the model
cost = T.mean(output.cross_entropy_binary(y))
#Defining params
params = lstm_1.params + output.params
ahead = T.matrix('ahead')
sent = T.matrix('sentence')
phonemes = T.imatrix('phonemes')
rng = numpy.random.RandomState(1234)
init_reg = LinearRegression(x, 60, 30,True)
lstm_1 = LSTM(rng,init_reg.E_y_given_x,30,lstm_1_hidden)
lstm_2 = LSTM(rng,lstm_1.output,lstm_1_hidden,lstm_2_hidden)
reg_input = lstm_2.output
#need log_reg and cross covariate layers
log_reg = LogisticRegression(reg_input,lstm_2_hidden, 41)
#lin_reg = LinearRegression(reg_input,lstm_2_hidden,1,True)
log_reg.reconstruct(log_reg.p_y_given_x)
#lin_reg.reconstruct(lin_reg.E_y_given_x)
#reconstructed_regressions = T.concatenate([log_reg.reconstructed_x,lin_reg.reconstructed_x],axis=1)
#
#reverse_layer = LinearRegression(reconstructed_regressions, 2*lstm_2_hidden, lstm_2_hidden,False)
lstm_3 = LSTM(rng,log_reg.reconstructed_x,lstm_2_hidden,lstm_1_hidden)
lstm_4 = LSTM(rng,lstm_3.output,lstm_1_hidden,30)
init_reg.reconstruct(lstm_4.output)
ahead = T.matrix('ahead')
sent = T.matrix('sentence')
phonemes = T.imatrix('phonemes')
rng = numpy.random.RandomState(1234)
init_reg = LinearRegression(x, 1, 30, True)
lstm_1 = LSTM(rng, init_reg.E_y_given_x, 30, lstm_1_hidden)
lstm_2 = LSTM(rng, lstm_1.output, lstm_1_hidden, lstm_2_hidden)
reg_input = lstm_2.output
#need log_reg and cross covariate layers
log_reg = LogisticRegression(reg_input, lstm_2_hidden, 41)
#lin_reg = LinearRegression(reg_input,lstm_2_hidden,1,True)
log_reg.reconstruct(log_reg.p_y_given_x)
#lin_reg.reconstruct(lin_reg.E_y_given_x)
#reconstructed_regressions = T.concatenate([log_reg.reconstructed_x,lin_reg.reconstructed_x],axis=1)
#
#reverse_layer = LinearRegression(reconstructed_regressions, 2*lstm_2_hidden, lstm_2_hidden,False)
lstm_3 = LSTM(rng, log_reg.reconstructed_x, lstm_2_hidden, lstm_1_hidden)
lstm_4 = LSTM(rng, lstm_3.output, lstm_1_hidden, 30)
init_reg.reconstruct(lstm_4.output)
poolsize=(1, 1),
dim2 = 1
)
layer3 = LeNetConvPoolLayer(
rng,
input=layer2.output,
image_shape=(minibatch_size, layer2_filters, 15, 15),
filter_shape=( layer3_filters, layer2_filters, 2, 2),
poolsize=(1, 1),
dim2 = 1
)
reg_input = layer3.output.flatten(2)
log_reg = LogisticRegression(reg_input,15*15*layer3_filters, 41)
lin_reg = LinearRegressionRandom(reg_input,15*15*layer3_filters,2,True)
log_input = log_reg.p_y_given_x
lin_input = lin_reg.E_y_given_x
log_reg.reconstruct(log_input)
lin_reg.reconstruct(lin_input)
reconstructed_regressions = T.concatenate([log_reg.reconstructed_x,lin_reg.reconstructed_x],axis=1)
reverse_layer = LinearRegression(reconstructed_regressions, 2*15*15*layer3_filters, 15*15*layer3_filters,False)
reconstruct = reverse_layer.E_y_given_x.reshape((minibatch_size,layer3_filters,15,15))
2),
poolsize=(1, 1),
dim2=1)
layer3 = LeNetConvPoolLayer(rng,
input=layer2.output,
image_shape=(minibatch_size, layer2_filters, 15,
15),
filter_shape=(layer3_filters, layer2_filters, 2,
2),
poolsize=(1, 1),
dim2=1)
reg_input = layer3.output.flatten(2)
log_reg = LogisticRegression(reg_input, 15 * 15 * layer3_filters, 41)
lin_reg = LinearRegressionRandom(reg_input, 15 * 15 * layer3_filters, 2, True)
log_input = log_reg.p_y_given_x
lin_input = lin_reg.E_y_given_x
log_reg.reconstruct(log_input)
lin_reg.reconstruct(lin_input)
reconstructed_regressions = T.concatenate(
[log_reg.reconstructed_x, lin_reg.reconstructed_x], axis=1)
reverse_layer = LinearRegression(reconstructed_regressions,
2 * 15 * 15 * layer3_filters,
15 * 15 * layer3_filters, False)
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as a vector of inputs with many exchangeable examples of this vector
x = clip_gradient(x,1.0)
y = T.matrix('y') # the data is presented as a vector of inputs with many exchangeable examples of this vector
is_train = T.iscalar('is_train') # pseudo boolean for switching between training and prediction
rng = numpy.random.RandomState(1234)
# Architecture: input --> LSTM --> predict one-ahead
lstm_1 = LSTM(rng, x, n_in=data_set_x.get_value(borrow=True).shape[1], n_out=n_hidden)
output = LogisticRegression(input=lstm_1.output, n_in=n_hidden, n_out=data_set_x.get_value(borrow=True).shape[1])
################################
# Objective function and GD
################################
print 'defining cost, parameters, and learning function...'
# the cost we minimize during training is the negative log likelihood of
# the model
cost = T.mean(output.cross_entropy_binary(y))
#Defining params
params = lstm_1.params + output.params
