'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
trial_no = T.matrix('trial_no')
rng = numpy.random.RandomState(1234)
# Architecture: input --> CNN (Pooling only on frequency) --> LSTM --> Poisson Regression on Neural data
layer0_input = x.reshape((1, 1, song_size - 1, 60))
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(1, 1, song_size - 1, 60),
filter_shape=(filter_number_1, 1, 1, 3),
poolsize=(1, 3),
dim2=1)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(1, filter_number_1, song_size - 1,
20),
filter_shape=(filter_number_2, filter_number_1, 1,
2),
poolsize=(1, 2),
dim2=1)
lstm_input = layer1.output.reshape((song_size - 1, 10 * filter_number_2))
#May be worth splitting to different LSTMs...would require smaller filter size
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
layer0_input = x.reshape((1, 1, song_size-1, 60))
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(1, 1, song_size-1, 60),
filter_shape=( filter_number, 1, 1, 10),
poolsize=(1, 3),
dim2 = 1
)
lstm_input = layer0.output.reshape((song_size-1,20 * filter_number))
#May be worth splitting to different LSTMs...would require smaller filter size
lstm_1 = LSTM(rng, lstm_input, n_in=20 * filter_number, n_out=n_hidden)
#output = LinearRegression(input=lstm_1.output, n_in=n_hidden, n_out=data_set_x.get_value(borrow=True).shape[1])
lstm_output = lstm_1.output.reshape((1,filter_number,song_size-1,20))
layer0.reverseConv(lstm_output,(1,filter_number,song_size-1,60),(1,filter_number,1,10)) #filter flipped on first two axes
# 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
rng = numpy.random.RandomState(1234)
# Reshape matrix of rasterized images of shape (batch_size, 2000 * 60)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((minibatch_size, 1, 1000, 60))
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(minibatch_size, 1, 1000, 60),
filter_shape=(layer0_filters, 1, 5, 5),
poolsize=(5, 1),
dim2=1)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(minibatch_size, layer0_filters, 200,
60),
filter_shape=(layer1_filters, layer0_filters, 2,
2),
poolsize=(2, 1),
dim2=1)
layer2 = LeNetConvPoolLayer(rng,
input=layer1.output,
image_shape=(minibatch_size, layer1_filters, 100,
) # the data is presented as a vector of inputs with many exchangeable examples of this vector
y = T.imatrix(
'y'
) # the data is presented as a vector of inputs with many exchangeable examples of this vector
y_enc = T.imatrix('y_enc')
rng = numpy.random.RandomState(1234)
number = T.matrix('number')
variation = T.matrix('variation')
# Reshape matrix of rasterized images of shape (batch_size, 2000 * 60)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
layer0_input = x.reshape((minibatch_size, 1, 60, 60))
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(minibatch_size, 1, 60, 60),
filter_shape=(layer0_filters, 1, 3, 3),
poolsize=(2, 2),
dim2=1)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(minibatch_size, layer0_filters, 30,
30),
filter_shape=(layer1_filters, layer0_filters, 2,
2),
poolsize=(2, 2),
dim2=1)
layer2 = LeNetConvPoolLayer(rng,
input=layer1.output,
image_shape=(minibatch_size, layer1_filters, 15,
