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Fix mostly syntax problems #4

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Fix mostly syntax problems #4

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4 changes: 3 additions & 1 deletion ST_LSTM.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,8 @@

import math

DIM_value = "" #?

class SpatioTemporal_LSTM(nn.Module):
"""docstring for SpatioTemporal_LSTM"""
def __init__(self, hidden_size, input_size):
Expand Down Expand Up @@ -70,7 +72,7 @@ def _compute_cell(self, x, h, c, M):

o = torch.sigmoid(F.conv2d(x, self.weight_xo) + F.conv2d(M, self.weight_mo) + F.conv2d(c, self.weight_co) + F.conv2d(h, self.weight_ho) + self.bias_o)

h = o * torch.tanh(F.conv2d(torch.cat((c,M), dim= ),self.weight_1x1))
h = o * torch.tanh(F.conv2d(torch.cat((c,M), dim=DIM_value ),self.weight_1x1))

return h,c,M

Expand Down
6 changes: 3 additions & 3 deletions data_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ def __init__(self, data_pb):
try:
f = h5py.File('/home/ujjax/Downloads/mnist.h5')
except:
print 'Please set the correct path to MNIST dataset'
print('Please set the correct path to MNIST dataset')
sys.exit()

self.data_ = f['train'].value.reshape(-1, 28, 28)
Expand Down Expand Up @@ -152,7 +152,7 @@ def DisplayData(self, data, rec=None, fut=None, fig=1, case_id=0, output_file=No
plt.axis('off')
plt.draw()
if output_file1 is not None:
print output_file1
print(output_file1)
plt.savefig(output_file1, bbox_inches='tight')

# create figure for reconstuction and future sequences
Expand All @@ -168,7 +168,7 @@ def DisplayData(self, data, rec=None, fut=None, fig=1, case_id=0, output_file=No
plt.axis('off')
plt.draw()
if output_file2 is not None:
print output_file2
print(output_file2)
plt.savefig(output_file2, bbox_inches='tight')
else:
plt.pause(0.1)
46 changes: 23 additions & 23 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,20 +21,20 @@ def __init__(self):
self.H = dict()

self.h = dict()
self.c = dict()
self.m = { 0 : Parameter(torch.Tensor())}
self.c = dict()
self.m = { 0 : Parameter(torch.Tensor())}


self.h[0] = Parameter(torch.Tensor(shape))
self.h[1] = Parameter(torch.Tensor(shape))
self.h[2] = Parameter(torch.Tensor(shape))
self.h[3] = Parameter(torch.Tensor(shape))
self.h[0] = Parameter(torch.Tensor(shape))
self.h[1] = Parameter(torch.Tensor(shape))
self.h[2] = Parameter(torch.Tensor(shape))
self.h[3] = Parameter(torch.Tensor(shape))


self.c[0] = Parameter(torch.Tensor(shape))
self.c[1] = Parameter(torch.Tensor(shape))
self.c[2] = Parameter(torch.Tensor(shape))
self.c[3] = Parameter(torch.Tensor(shape))
self.c[0] = Parameter(torch.Tensor(shape))
self.c[1] = Parameter(torch.Tensor(shape))
self.c[2] = Parameter(torch.Tensor(shape))
self.c[3] = Parameter(torch.Tensor(shape))

self.cells = nn.ModuleList([])
for i in self.n_layers:
Expand All @@ -44,9 +44,9 @@ def __init__(self):
self._reset_parameters()

def _reset_parameters(self):
stdv = 1.0 / math.sqrt(self.hidden_size)
for weight in self.parameters():
weight.data.uniform_(-stdv, stdv)
stdv = 1.0 / math.sqrt(self.hidden_size)
for weight in self.parameters():
weight.data.uniform_(-stdv, stdv)


def forward(self, input_, first_timestep = False):
Expand All @@ -68,11 +68,11 @@ def forward(self, input_, first_timestep = False):
return self.H , self. C, self.M

def initHidden(self):
result = Variable(torch.zeros(1, 1, self.hidden_size)) #################SHAPE
if use_cuda:
return result.cuda()
else:
return result
result = Variable(torch.zeros(1, 1, self.hidden_size)) #################SHAPE
if use_cuda:
return result.cuda()
else:
return result


class Decoder(nn.Module):
Expand Down Expand Up @@ -105,8 +105,8 @@ def forward(self, input_, C,H,M):
return output

def initHidden(self):
result = Variable(torch.zeros(1, 1, self.hidden_size))
if use_cuda:
return result.cuda()
else:
return result
result = Variable(torch.zeros(1, 1, self.hidden_size))
if use_cuda:
return result.cuda()
else:
return result
39 changes: 20 additions & 19 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,8 @@

SOS_token = 0
EOS_token = 1

FILE_PATH = '' #¿?
MAX_LENGTH = '' #¿?¿?

def showPlot(points):
plt.figure()
Expand All @@ -41,9 +42,9 @@ def timeSince(since, percent):


def train(input_variable, target_variable, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH,teacher_forcing_ratio = 0.5):
encoder_hidden = encoder.initHidden()
encoder_hidden = encoder.initHidden()

encoder_optimizer.zero_grad()
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()

input_length = input_variable.size()[0]
Expand Down Expand Up @@ -76,10 +77,10 @@ def train(input_variable, target_variable, encoder, decoder, encoder_optimizer,

else:
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_outputs)

topv, topi = decoder_output.data.topk(1)
topv, topi = decoder_output.data.topk(1)
ni = topi[0][0]

decoder_input = Variable(torch.LongTensor([[ni]]))
Expand All @@ -98,10 +99,10 @@ def train(input_variable, target_variable, encoder, decoder, encoder_optimizer,


def trainIters(encoder, decoder, n_iters,data_generator, print_every=1000, plot_every=100, learning_rate=0.01):
start_time = time.time()
start_time = time.time()


encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)

training_pairs = [variablesFromPair(random.choice(pairs))
Expand All @@ -112,7 +113,7 @@ def trainIters(encoder, decoder, n_iters,data_generator, print_every=1000, plot_


for it in range(1, n_iters+1):
training_pair = next(data_generator)
training_pair = next(data_generator)
input_variable = training_pair[0]
target_variable = training_pair[1]

Expand Down Expand Up @@ -176,23 +177,23 @@ def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):


def main(_):
batch_size = 128
file_path = #####
batch_size = 128
file_path = FILE_PATH

data = prepare_data.load_data(file_path)
data = prepare_data.load_data(file_path)

data_generator = prepare_data.get_batches(data, batch_size)
data_generator = prepare_data.get_batches(data, batch_size)

hidden_size = 256
encoder1 = Encoder(input_lang.n_words, hidden_size)
decoder1 = Decoder(hidden_size, output_lang.n_words, dropout_p=0.1)
hidden_size = 256
encoder1 = Encoder(input_lang.n_words, hidden_size)
decoder1 = Decoder(hidden_size, output_lang.n_words, dropout_p=0.1)


if use_cuda:
encoder1 = encoder1.cuda()
attn_decoder1 = decoder1.cuda()
if use_cuda:
encoder1 = encoder1.cuda()
attn_decoder1 = decoder1.cuda()

trainIters(encoder1, decoder1, 75000, print_every=5000)
trainIters(encoder1, decoder1, 75000, print_every=5000)


if __name__ == '__main__':
Expand Down