def score(self, input_data, label):
input_pad = self.prepare_input(input_data)
label = self.prepare_label(label)
prediction = self.model.predict(input_pad)
get_score_senti(label, prediction)
get_confusion_matrix(label,
prediction) #implemented cofusion matrix for it
def train(self,
train_input_data,
train_label,
valid_input_data,
valid_label,
class_weights=None):
x_train = self.prepare_input(train_input_data)
y_train = self.prepare_label(train_label)
# np.set_printoptions(threshold=np.inf)
x_valid = self.prepare_input(valid_input_data)
y_valid = self.prepare_label(valid_label)
print('start training...')
history = self.model.fit(x=x_train,
y=y_train,
batch_size=self.config.batch_size,
epochs=self.config.n_epochs,
validation_data=(x_valid, y_valid),
callbacks=self.callbacks,
class_weight=class_weights)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.savefig('model_acc.png')
print('training end...')
print('score over valid data:')
valid_pred = self.model.predict(x_valid)
get_score_senti(y_valid, valid_pred)
def score(self, input_data, label):
input_pad = self.prepare_input(input_data)
label = self.prepare_label(label)
prediction = self.model.predict(input_pad)
# no need to print them
# print(prediction)
get_score_senti(label, prediction)
def train(self, train_input_data, train_label, valid_input_data,
valid_label):
x_train = self.prepare_input(train_input_data)
y_train = self.prepare_label(train_label)
x_valid = self.prepare_input(valid_input_data)
y_valid = self.prepare_label(valid_label)
print('start training...')
self.model.fit(x=x_train,
y=y_train,
batch_size=self.config.batch_size,
epochs=self.config.n_epochs,
validation_data=(x_valid, y_valid),
callbacks=self.callbacks)
print('training end...')
print('score over valid data:')
valid_pred = self.model.predict(x_valid)
get_score_senti(y_valid, valid_pred)
def train(self, train_input_data, train_label, valid_input_data, valid_label,class_weights=None,imBalanced=False):
print("LENGTH OF THIS STUPID input thing IS",len(train_input_data))
x_train = self.prepare_input(train_input_data)
y_train = self.prepare_label(train_label)
# np.set_printoptions(threshold=np.inf)
x_valid = self.prepare_input(valid_input_data)
y_valid = self.prepare_label(valid_label)
# if imBalanced is True:
# # print("Applying ImBalance Technique")
# # training_generator, steps_per_epoch = balanced_batch_generator(
# # x_train, y_train, sampler=NearMiss(), batch_size=10, random_state=42)
if imBalanced is False:
print('start training (without ImBalance)...')
history = self.model.fit(x=x_train, y=y_train, batch_size=self.config.batch_size, epochs=self.config.n_epochs,
validation_data=(x_valid, y_valid), callbacks=self.callbacks, class_weight=class_weights)
else:
print('start training (without ImBalance)...')
# history = self.model.fit_generator(generator=training_generator,steps_per_epoch=steps_per_epoch, batch_size=self.config.batch_size, epochs=self.config.n_epochs,
# validation_data=(x_valid, y_valid), callbacks=self.callbacks, class_weight=class_weights)
history = self.model.fit(x=(x_train[0],x_train[1]), y=y_train, batch_size=self.config.batch_size, epochs=self.config.n_epochs,
validation_data=(x_valid, y_valid), callbacks=self.callbacks, class_weight=class_weights)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.savefig('model_acc.png')
print('training end...')
print('score over valid data:')
valid_pred = self.model.predict(x_valid)
get_score_senti(y_valid, valid_pred)
def on_epoch_end(self, epoch, logs={}):
if len(self.validation_data[:-3]) == 1:
x_valid = self.validation_data[0]
else:
x_valid = self.validation_data[:-3]
y_valid = self.validation_data[-3]
valid_results = self.model.predict(x_valid)
_val_acc, _val_f1 = get_score_senti(y_valid, valid_results)
logs['val_acc'] = _val_acc
logs['val_f1'] = _val_f1
self.val_accs.append(_val_acc)
self.val_f1s.append(_val_f1)
print('val_acc: %f' % _val_acc)
print('val_f1: %f' % _val_f1)
return
def score(self, input_data, label):
input_pad = self.prepare_input(input_data)
label = self.prepare_label(label)
prediction = self.model.predict(input_pad)
get_score_senti(label, prediction)
