def main():
if not os.path.isfile('X_train.npy'):
X, y = io.load_pictures('../../data/characters/', True)
X_train, X_test, y_train, y_test = io.split_data(X, y, 85)
io.write_data_to_file(X_train, X_test, y_train, y_test)
else:
X_train, X_test, y_train, y_test = io.load_data_from_file()
#Train and test
run_train_and_test(X_train, X_test, y_train, y_test)
def main():
if not os.path.isfile('X_train.npy'):
X,y = io.load_pictures('../../data/characters/', True)
X_train, X_test, y_train, y_test = io.split_data(X, y, 85)
io.write_data_to_file(X_train, X_test, y_train, y_test)
else:
X_train, X_test, y_train, y_test = io.load_data_from_file()
logging.getLogger().setLevel(logging.DEBUG)
batch_size = 10
print "\n start train autoencoder 1..."
autoencoder1 = autoencoder_one(X_train, batch_size)
input_for_autoencoder2 = forward_autoencoder_one(autoencoder1, X_train)
print "\n start train autoencoder 2..."
autoencoder2 = autoencoder_two(input_for_autoencoder2, batch_size)
print " \n start train fcnn ..."
run_train_and_test_fcnn(X_train, X_test, y_train, y_test, batch_size, autoencoder1, autoencoder2)
def main():
get_model('http://data.mxnet.io/models/imagenet/resnet/18-layers/resnet-18', 0)
sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-18', 0)
#print (sym.get_internals())
batch_size = 10
num_classes = 18
if not os.path.isfile('X_train.npy'):
X,y = io.load_pictures('characters/', True)
X_train, X_test, y_train, y_test = io.split_data(X, y, 85)
io.write_data_to_file(X_train, X_test, y_train, y_test)
else:
X_train, X_test, y_train, y_test = io.load_data_from_file()
train_iter, val_iter = get_iterators(X_train, y_train, X_test, y_test, batch_size)
mod_score = fit(sym, train_iter, val_iter, batch_size)
print (mod_score)