with open(prms_file_name, 'r') as p_fp:
params = ast.literal_eval(p_fp.read())
layers = params['layers']
tr_prms = params['training_params']
############################################## Load
print('Loading Data')
try:
img_sz = layers[0][1]["img_sz"]
except KeyError:
img_sz = layers[0][1]["img_sz"] = 32
pad_width = (img_sz-32)//2
data_x, data_y = utils.load_pad_info(data_file, pad_width, 0)
corpus_sz = data_x.shape[0]
# Print top three layers
for lyr, prms in layers[:3]:
print(lyr)
for param, value in prms.items():
print(" {:20}:{}".format(param, value))
print()
############################################## Init Layer
imgs = shared(np.asarray(data_x, config.floatX), borrow=True)
x_sym = tt.tensor4('x')
net_layers, i = [], 0
lyr_look_up = {"InputLayer":InputLayer,
"ElasticLayer":ElasticLayer,
with open(nnet_prms_file_name, 'rb') as nnet_prms_file:
params = pickle.load(nnet_prms_file)
layers = params['layers']
tr_prms = params['training_params']
allwts = params['allwts']
############################################# Load Data
print("\nLoading the data ...")
try:
img_sz = layers[0][1]["img_sz"]
except KeyError:
img_sz = layers[0][1]["img_sz"] = 32
pad_width = (img_sz - 32) // 2
trin_x, trin_y = utils.load_pad_info("train", pad_width, 0)
test_x, test_y = utils.load_pad_info("test", pad_width, 0)
############################################# Init Network
params['training_params']['BATCH_SZ'] = batch_sz = 100
ntwk = NeuralNet(**params)
############################################# Read glyphs & classify
coarse = utils.fine_to_coarse
def test_wrapper(tester, truth):
print("Classifying...")
fine_errors = np.zeros((100, 100), dtype="uint")
coarse_errors = np.zeros((20, 20), dtype="uint")
sym_err, bit_err, n = 0., 0., len(truth) // batch_sz
with open(prms_file_name, 'r') as p_fp:
params = ast.literal_eval(p_fp.read())
layers = params['layers']
tr_prms = params['training_params']
############################################## Load
print('Loading Data')
try:
img_sz = layers[0][1]["img_sz"]
except KeyError:
img_sz = layers[0][1]["img_sz"] = 32
pad_width = (img_sz - 32) // 2
data_x, data_y = utils.load_pad_info(data_file, pad_width, 0)
corpus_sz = data_x.shape[0]
# Print top three layers
for lyr, prms in layers[:3]:
print(lyr)
for param, value in prms.items():
print(" {:20}:{}".format(param, value))
print()
############################################## Init Layer
imgs = shared(np.asarray(data_x, config.floatX), borrow=True)
x_sym = tt.tensor4('x')
net_layers, i = [], 0
lyr_look_up = {
"InputLayer": InputLayer,
print('Host :', socket.gethostname())
print(nn.get_layers_info(layers))
print(nn.get_training_params_info(tr_prms))
########################################## Load Data
print("\nLoading the data ...")
try:
img_sz = layers[0][1]["img_sz"]
except KeyError:
img_sz = layers[0][1]["img_sz"] = 32
pad_width = (img_sz-32)//2
trin_x, trin_y = utils.load_pad_info("train", pad_width, 0)
test_x, test_y = utils.load_pad_info("test", pad_width, 0)
batch_sz = tr_prms['BATCH_SZ']
n_train = len(trin_y)
n_test = len(test_y)
trin_x = share(trin_x)
test_x = share(test_x)
trin_y = share(trin_y, 'int32')
test_y = share(test_y, 'int32')
################################################
print("\nInitializing the net ... ")
net = nn.NeuralNet(layers, tr_prms, allwts)
print(net)
params = pickle.load(nnet_prms_file)
layers = params['layers']
tr_prms = params['training_params']
allwts = params['allwts']
############################################# Load Data
print("\nLoading the data ...")
try:
img_sz = layers[0][1]["img_sz"]
except KeyError:
img_sz = layers[0][1]["img_sz"] = 32
pad_width = (img_sz-32)//2
#trin_x, trin_y = utils.load_pad_info("train", pad_width, 0)
test_x, test_y = utils.load_pad_info("test", pad_width, 0)
test_col = (np.rollaxis(test_x, 1, 4)*255).astype("uint8")
############################################# Init Network
params['training_params']['BATCH_SZ'] = 1
ntwk = NeuralNet(**params)
tester = ntwk.get_data_test_model(go_nuts=True)
############################################# Image saver
dir_name = os.path.basename(nnet_prms_file_name)[:-4] + '/'
if not os.path.exists(dir_name):
os.makedirs(dir_name)
namer = (dir_name + '{}_{:02d}.png').format
# Usage:namer(info, i)
