config_num = int(sys.argv[2])
if len(sys.argv) > 3:
log_space = sys.argv[3][0] in "TtYy1"
################################
# Network Parameters
midlayer, midlayerargs = configs[config_num]
chars = data['chars']
nClasses = len(chars)
nDims = len(data['x'][0])
nSamples = len(data['x'])
nTrainSamples = nSamples * .75
nEpochs = 100
labels_print, labels_len = prediction_printer(chars)
print("\nConfig: {}"
"\n Midlayer: {} {}"
"\nInput Dim: {}"
"\nNum Classes: {}"
"\nNum Samples: {}"
"\nFloatX: {}"
"\nUsing log space: {}"
"\n".format(config_num, midlayer, midlayerargs, nDims, nClasses,
nSamples, th.config.floatX, log_space))
################################
print("Preparing the Data")
try:
conv_sz = midlayerargs["conv_sz"]
data = pickle.load(pkl_file)
try:
nHidden = int(sys.argv[2])
except IndexError:
pass
nClasses = data['nChars']
nDims = len(data['x'][0])
nSamples = len(data['x'])
nTrainSamples = nSamples * .75
ntwk = RnnCTC(nDims, nHidden, nClasses)
train_fn = ntwk.get_train_fn()
test_fn = ntwk.get_test_fn()
pred_print = prediction_printer(nClasses)
data_x, data_y = [], []
for x, y in zip(data['x'], data['y']):
# Need to make alternate characters blanks (index as nClasses)
y1 = [nClasses]
for char in y:
y1 += [char, nClasses]
data_y.append(np.asarray(y1, dtype=np.int32))
data_x.append(np.asarray(x, dtype=theano.config.floatX))
# Actual training
for epoch in range(100):
print('Epoch : ', epoch)
for samp in range(nSamples):
x = data_x[samp]
config_num = int(sys.argv[2])
if len(sys.argv) > 3:
log_space = sys.argv[3][0] in "TtYy1"
################################
# Network Parameters
midlayer, midlayerargs = configs[config_num]
chars = data['chars']
nClasses = len(chars)
nDims = len(data['x'][0])
nSamples = len(data['x'])
nTrainSamples = nSamples * .75
nEpochs = 100
labels_print, labels_len = prediction_printer(chars)
print("\nConfig: {}"
"\n Midlayer: {} {}"
"\nInput Dim: {}"
"\nNum Classes: {}"
"\nNum Samples: {}"
"\nFloatX: {}"
"\nUsing log space: {}"
"\n".format(config_num, midlayer, midlayerargs, nDims, nClasses,
nSamples, th.config.floatX, log_space))
################################
print("Preparing the Data")
try:
conv_sz = midlayerargs["conv_sz"]
data = pickle.load(pkl_file)
if len(sys.argv) > 2:
config_num = int(sys.argv[2])
################################
# Network Parameters
midlayer, midlayerargs = configs[config_num]
nClasses = data['nChars']
nDims = len(data['x'][0])
nSamples = len(data['x'])
nTrainSamples = nSamples * .75
nEpochs = 100
labels_print, labels_len = prediction_printer(nClasses)
print("\nConfig {}"
"\n\tMidlayer: {} {}"
"\nInput Dim: {}"
"\nNum Classes: {}"
"\nNum Samples: {}"
"\n".format(config_num, midlayer, midlayerargs,
nDims, nClasses, nSamples))
################################
print("Preparing the Data")
try:
conv_sz = midlayerargs["conv_sz"]
except KeyError:
conv_sz = 1
