def load_custom_gru():
root.update()
filename = filedialog.askopenfilename(initialdir = os.getcwd(), title = 'Select file', filetypes=[('Numpy Files', '*.npz')])
if filename != "":
try:
model = GRU(dictionary_size)
log_message = model.load_model_parameters(filename)
generate_sonnet1_button['state'] = 'normal'
generate_sonnet2_button['state'] = 'normal'
bigram_flag = False
write_to_log(log_message, logger_index)
current_model.set("Current Model: Custom GRU")
except(FileNotFoundError):
write_to_log("No such file or directory: %s" %filename, logger_index)
return
def load_custom_gru():
root.update()
filename = filedialog.askopenfilename(initialdir = os.getcwd(), title = 'Select file', filetypes=[('Numpy Files', '*.npz')])
if filename != "":
try:
model = GRU(dictionary_size)
log_message = model.load_model_parameters(filename)
generate_sonnet1_button['state'] = 'normal'
generate_sonnet2_button['state'] = 'normal'
bigram_flag = False
write_to_log(log_message, logger_index)
current_model.set("Current Model: Custom GRU")
except(IOError):
write_to_log("No such file or directory: %s" %filename, logger_index)
return
def load_gru():
iterations = number_iterations.get()
if iterations == 20:
filename = "GRUModel20.npz"
elif iterations == 40:
filename = "GRUModel40.npz"
elif iterations == 60:
filename = "GRUModel60.npz"
elif iterations == 80:
filename = "GRUModel80.npz"
else:
filename = "GRUModel100.npz"
model = GRU(dictionary_size)
log_message = model.load_model_parameters("TrainedModels/" + filename)
generate_sonnet1_button['state'] = 'normal'
generate_sonnet2_button['state'] = 'normal'
generate_sonnet1_image_button['state'] = 'normal'
generate_sonnet2_image_button['state'] = 'normal'
bigram_flag = False
write_to_log(log_message + " iterations=%d" % iterations, logger_index)
current_model.set("Current Model: GRU %d iterations" % iterations)
def load_gru():
iterations = number_iterations.get()
if iterations == 20:
filename = "GRUModel20.npz"
elif iterations == 40:
filename = "GRUModel40.npz"
elif iterations == 60:
filename = "GRUModel60.npz"
elif iterations == 80:
filename = "GRUModel80.npz"
else:
filename = "GRUModel100.npz"
model = GRU(dictionary_size)
log_message = model.load_model_parameters("TrainedModels/" + filename)
generate_sonnet1_button['state'] = 'normal'
generate_sonnet2_button['state'] = 'normal'
generate_sonnet1_image_button['state'] = 'normal'
generate_sonnet2_image_button['state'] = 'normal'
bigram_flag = False
write_to_log(log_message + " iterations=%d" %iterations, logger_index)
current_model.set("Current Model: GRU %d iterations" %iterations)
for (num_examples, loss) in losses:
num_examples_array.append(num_examples)
loss_array.append(loss)
plt.plot(num_examples_array, loss_array)
plt.title('Losses of Trained RNN Model %d iterations' %
number_of_iterations)
plt.xlabel('number of examples seen')
plt.ylabel('losses')
plt.grid(True)
plt.show()
model.save_model_parameters(save_to_file)
print("Done training model")
# GRU
elif args.mode == "GRU":
print("Generating GRU Model...")
model = GRU(dictionary_size)
print("Training GRU Model with %d iterations..." % number_of_iterations)
losses = model.train_with_sgd(start_train,
end_train,
nepoch=number_of_iterations)
for (num_examples, loss) in losses:
num_examples_array.append(num_examples)
loss_array.append(loss)
plt.plot(num_examples_array, loss_array)
plt.title('Losses of Trained GRU Model %d iterations' %
number_of_iterations)
plt.xlabel('number of examples seen')
plt.ylabel('losses')
plt.grid(True)
plt.show()
model.save_model_parameters(save_to_file)