print "Saving model parameters"
model.save_model_params_dumb("../models/LSTM_ONLY_3_new" +
str(i + 1) + "_" + str(avg_loss) +
"_" + str(avg_acc) + ".pkl")
if __name__ == '__main__':
assert (len(argv) >= 4)
print "reading arguments"
train_samples_file = argv[1]
test_samples_file = argv[2]
embedding_file = argv[3]
print "Initializing data loading"
#loader_train1 = data_loader(train_samples_file, from_chunk = False)
#loader_train2 = data_loader(train_samples_file, from_chunk = False)
loader_train = data_loader_new(train_samples_file,
embedding_file=embedding_file)
samples_train_count = loader_train.samples_count
test_loader = data_loader_new(test_samples_file,
embedding_file=embedding_file)
samples_test_count = test_loader.samples_count
print "Initializing model"
model = LSTM(sequence_length=5)
print "Loading data"
X_train, Y_train = loader_train.load_sequence_samples(
num_elements=samples_train_count)
X_test, Y_test = test_loader.load_sequence_samples(
num_elements=samples_test_count)
if (i + 1)%3 == 0:
write_to_file("Calculating test performance after epoch " + str(i))
evaluate_single_chunk(model,[X_test,Y_test])
print "Saving model parameters"
model.save_model_params_dumb("../models/LSTM_ONLY_1_layer_new"+str(i+1)+"_"+str(avg_loss)+"_"+str(avg_acc)+".pkl")
if __name__ == '__main__':
assert(len(argv) >= 4)
print "reading arguments"
train_samples_file = argv[1]
test_samples_file = argv[2]
embedding_file = argv[3]
print "Initializing data loading"
#loader_train1 = data_loader(train_samples_file, from_chunk = False)
#loader_train2 = data_loader(train_samples_file, from_chunk = False)
loader_train = data_loader_new(train_samples_file, embedding_file=embedding_file)
samples_train_count = loader_train.samples_count
test_loader = data_loader_new(test_samples_file, embedding_file=embedding_file)
samples_test_count = test_loader.samples_count
print "Initializing model"
model = LSTM(sequence_length = 5)
print "Loading data"
X_train,Y_train = loader_train.load_sequence_samples(num_elements = samples_train_count)
X_test,Y_test = test_loader.load_sequence_samples(num_elements = samples_test_count)
loader_train = None
test_loader = None
for i in range(num_epoch):
for j in range(num_batch_per_epoch):
print("\nEpoch no. %s, batch_no. %s\n"%(i+1,j+1))
data = q_test.get()
evaluate_single_chunk_par(model,data,batch_size,load_at_once,test_data_gen)
return model
if __name__ == '__main__':
assert(len(argv) >= 2)
print "reading arguments"
train_samples_file = argv[1]
test_samples_file = argv[2]
print "Initializing data loading"
#loader_train1 = data_loader(train_samples_file, from_chunk = False)
#loader_train2 = data_loader(train_samples_file, from_chunk = False)
loader_train = data_loader_new(train_samples_file)
samples_train_count = loader_train.samples_count
test_loader = data_loader_new(test_samples_file)
samples_test_count = test_loader.samples_count
q_train = Queue()
q_test = Queue()
if os.fork() == 0:
while True:
if q_train.qsize() < 10:
X , Y = loader_train.load_sequence_samples(num_elements=200,transform=False)
#print "Loaded batch"
q_train.put([X,Y])
if os.fork() == 0:
while True:
