with open('xtrain_gray.npy', mode="rb") as f:
xtrain = np.load(f)
with open('ytrain_gray.npy', mode="rb") as f:
ytrain = np.load(f)
idxs = np.arange(len(xtrain)) # -> array([0, 1, ..., 9999])
np.random.shuffle(idxs)
x_train = []
y_train = []
for i in idxs:
x_train.append(xtrain[i])
y_train.append(ytrain[i])
x_train = np.array(x_train)
y_train = np.array(y_train)
model = Model2.Model()
lr = 0.01
mn = 0.9
wd = 5e-04
optimization = Adam(model.parameters, learning_rate=lr)
batch_size = 1000
idxs = np.arange(len(xtrain)) # -> array([0, 1, ..., 9999])
np.random.shuffle(idxs)
for batch_cnt in range(0, len(xtrain) // batch_size):
batch_indices = idxs[batch_cnt * batch_size: (batch_cnt + 1) * batch_size]
batch = xtrain[batch_indices] # random batch of our training data
# compute the predictions for this batch by calling on model
def main():
try:
global args
global canvas
global settings
controller = None
filemerge = None
view = None
model = None
settings.initialize_prefs(canvas.max_colors())
# Only POSIX systems are supported at this point
fileops = FileOpsPOSIX.FileOpsPOSIX()
if len(args) == 2:
left = args[0]
right = args[1]
left = os.path.realpath(left)
right = os.path.realpath(right)
# Instantiate the classes and link them together.
model = Model2.Model(fileops, left, right)
view = View2.View(canvas, model, settings)
else:
# len(args) == 3
left = args[0]
middle = args[1]
right = args[2]
left = os.path.realpath(left)
middle = os.path.realpath(middle)
right = os.path.realpath(right)
# Instantiate the classes and link them together.
model = Model3.Model(fileops, left, middle, right)
view = View3.View(canvas, model, settings)
requested_filemerge = settings.get_value('file_merge_program')
# print('file_merge_program:', requested_filemerge)
if requested_filemerge == 'vim':
filemerge = FileMergeVim.FileMergeVim()
else:
filemerge = FileMergeEmacs.FileMergeEmacs()
controller = Controller.Controller(model, view, canvas, filemerge)
# Start the model. It will enumerate and then start comparing.
model.start_enumerate()
# Main event loop
while not controller.need_to_quit:
if canvas.need_to_resize:
view.resize()
model.render_again = False
view.render()
if ((model.state() == Model2.STATE_NORMAL
or model.state() == Model3.STATE_NORMAL)
and not model.render_again and not canvas.need_to_resize):
input = canvas.get_input(0) # wait indefinitely
else:
input = canvas.get_input(1) # timeout in 1/10 second
controller.process_input(input)
finally:
# print("quitting")
if controller is not None:
controller.destroy()
if filemerge is not None:
filemerge.destroy()
if view is not None:
view.destroy()
if model is not None:
model.destroy()