def custom_fit(model, callbacks, x_train, y_train, x_test, y_test, epochs, batch_size,
dir_name, compare_title, draw_step=10, verbose=1):
np.random.seed(42)
epochs_step = int(epochs / draw_step)
if dir_name != None:
os.mkdir(dir_name)
full_loss_history = np.empty(0)
for init_epoch in np.arange(0, epochs, step=epochs_step):
save = False if dir_name == None else True
if save:
save_path = dir_name + "/" + "val_loss.png"
else:
save_path = None
history = model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=init_epoch + epochs_step,
verbose=verbose, callbacks=callbacks, validation_data=(x_test, y_test),
initial_epoch=init_epoch)
full_loss_history = np.append(full_loss_history, history.history['val_loss'])
plt.plot(np.transpose(x_test)[0], y_test, '.')
plt.plot(np.transpose(x_test)[0], model.predict(x_test), '.')
plt.legend(('function', 'approximation'), loc='upper left', shadow=True)
plt.title(
compare_title + "\nval_loss = %.4f\nepoch = %d" % (history.history["val_loss"][history.epoch.__len__() - 1],
init_epoch + epochs_step))
if dir_name != None:
plt.savefig(dir_name + "/" + "%.d_compare_%.4f.png" %
(init_epoch + epochs_step, history.history["val_loss"][history.epoch.__len__() - 1])
, dpi=200)
plt.show()
plt.close()
if (history.epoch.__len__() - 1 != epochs_step):
epochs=init_epoch+history.epoch.__len__()
break
gr.plot_graphic(x=np.arange(1, epochs + 1), y=full_loss_history,
x_label='epochs', y_label='val_loss',
title="val_loss" + ' history', save_path=save_path, save=save, show=True)
return model
# <neur_number> neurons with 1024 inputs,initialize - normal distribution
model.add(
Dense(neur_number,
input_dim=in_image_size[0] * in_image_size[1],
init='normal',
activation='relu'))
model.add(Dense(1, init='normal', activation='hard_sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=SGD(lr=0.0008),
metrics=['accuracy'])
# batch_size define speed of studying
history = model.fit(x_train,
y_train,
batch_size=1,
nb_epoch=5,
verbose=1)
score = model.evaluate(x_test, y_test, verbose=1)
print("accuracy on testing data %.f%%" % (score[1] * 100))
gr.plot_history_separte(history,
save_path_acc="ACC.png",
save_path_loss="LOSS.png",
save=False,
show=True)
# model.save('CZ_REC_200.h5')
# 3 setting stopper
# callbacks.EarlyStopping(monitor='acc', min_delta=0, patience=5, mode='max')
callbacks = [EarlyStoppingByLossVal(monitor='val_loss', value=goal_loss, verbose=1)]
# 4 model fitting
model.compile(optimizer=optimizer, loss='mse', metrics=['mse'])
history = model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs,
verbose=verbose, callbacks=callbacks, validation_data=(x_test, y_test))
# Save information about learning and save NN
dir_name = "E_" + opt_name + "_" + str(history.epoch.__len__()) + "_" + str(lr) + str()
# os.mkdir(dir_name)
gr.plot_graphic(x=history.epoch, y=np.array(history.history["val_loss"]), x_label='epochs', y_label='val_loss',
title="val_loss" + ' history', save_path=dir_name + "/" + "val_loss.png", save=False, show=True)
plt_x_zero = np.empty(0)
plt_y_zero = np.empty(0)
plt_x_one = np.empty(0)
plt_y_one = np.empty(0)
plt_x_two = np.empty(0)
plt_y_two = np.empty(0)
plt_x_three = np.empty(0)
plt_y_three = np.empty(0)
plt_x_four = np.empty(0)
plt_y_four = np.empty(0)
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=verbose)
score = model.evaluate(x_test, y_test, verbose=verbose)
print("\nabsolute_error on train data\t %.f%%" %
(history.history['mean_absolute_error'][epochs - 1] * 100))
print("\nabsolute_error on testing data %.f%%" % (score[1] * 100))
print("loss on train data %.f%%" %
(history.history['loss'][epochs - 1] * 100))
gr.plot_graphic(x=history.epoch,
y=np.array(history.history['loss']),
x_label='epochs',
y_label='loss',
title='mean_squared_error history',
show=True)
gr.plot_graphic(x=history.epoch,
y=np.array(history.history['mean_absolute_error']),
x_label='epochs',
y_label='accuracy',
title='mean_absolute_error history',
show=True)
plt.plot(np.append(x_train, x_test),
model.predict(np.append(x_train, x_test)), '.')
plt.plot(np.append(x_train, x_test), np.append(y_train, y_test), '.')
plt.legend(('approximation', 'function'), loc='upper left', shadow=True)
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=verbose)
score = model.evaluate(x_test, y_test, verbose=verbose)
print("\nabsolute_error on train data\t %.f%%" %
(history.history['mean_absolute_error'][epochs - 1] * 100))
print("\nabsolute_error on testing data %.f%%" % (score[1] * 100))
print("loss on train data %.f%%" %
(history.history['loss'][epochs - 1] * 100))
gr.plot_history_separte(history=history,
acc='mean_absolute_error',
save_path_acc="ACC.png",
save_path_loss="LOSS.png",
save=True,
show=True)
plt.plot(np.append(x_train, x_test),
model.predict(np.append(x_train, x_test)), '.')
plt.plot(np.append(x_train, x_test), np.append(y_train, y_test), '.')
plt.legend(('approximation', 'function'), loc='upper left', shadow=True)
plt.show()
plt.close()
h = 0.05
count = 0