def fit(self, train_x, train_y, validation_data_fit, epochs, **kwargs): val_x, val_y = validation_data_fit patience = 2 callbacks = [ keras.callbacks.EarlyStopping(monitor='val_loss', patience=patience) ] if self._is_multilabel: train_y = train_y val_y = val_y else: train_y = ohe2cat(train_y) val_y = ohe2cat(val_y) self._model.fit( train_x, train_y, epochs=epochs, callbacks=callbacks, # validation_data=(val_x, val_y), verbose=1, # Logs once per epoch. batch_size=32, shuffle=True, # use_multiprocessing=True )
def fit(self, train_x, train_y, validation_data_fit, params, epochs, **kwargs): patience = 2 callbacks = [ keras.callbacks.EarlyStopping(monitor='val_loss', patience=patience) ] val_x, val_y = validation_data_fit if self._class_num == 2: train_y = ohe2cat(train_y) val_y = ohe2cat(val_y) batch_size = params["batch_size"] steps_per_epoch = int(len(train_x) // batch_size) train_data_generator = ModelSequenceDataGenerator( train_x, train_y, **params) history = self._model.fit_generator(train_data_generator, steps_per_epoch=steps_per_epoch, validation_data=(val_x, val_y), epochs=epochs, max_queue_size=10, callbacks=callbacks, use_multiprocessing=False, workers=1, verbose=VERBOSE) return history
def fit(self, train_x, train_y, validation_data_fit, epochs, batch_size=32, **kwargs): val_x, val_y = validation_data_fit callbacks = [] if epochs > 1: early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3) callbacks.append(early_stop) self._model.fit( train_x, ohe2cat(train_y), epochs=epochs, callbacks=callbacks, validation_data=(val_x, ohe2cat(val_y)), verbose=1, # Logs once per epoch. batch_size=batch_size, shuffle=True) else: self._model.fit( train_x, ohe2cat(train_y), epochs=1, callbacks=callbacks, verbose=1, # Logs once per epoch. batch_size=batch_size, shuffle=True)
def fit(self, train_x, train_y, validation_data_fit, epochs, **kwargs): val_x, val_y = validation_data_fit callbacks = [keras.callbacks.EarlyStopping( monitor='val_loss', patience=3)] self._model.fit(train_x, ohe2cat(train_y), epochs=epochs, callbacks=callbacks, validation_data=(val_x, ohe2cat(val_y)), verbose=1, # Logs once per epoch. batch_size=32, shuffle=True)
def fit(self, train_x, train_y, validation_data_fit, epochs, cur_model_run_loop, batch_size=32, **kwargs): val_x, val_y = validation_data_fit callbacks = [] if self._use_step_decay: lrate = LearningRateScheduler(self.step_decay) callbacks.append(lrate) if self._is_multilabel: train_y = train_y val_y = val_y else: train_y = ohe2cat(train_y) val_y = ohe2cat(val_y) if epochs > 1: early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3) callbacks.append(early_stop) self._model.fit( train_x, train_y, epochs=epochs, callbacks=callbacks, validation_data=(val_x, val_y), verbose=1, # Logs once per epoch. batch_size=batch_size, shuffle=True) else: self._model.fit( train_x, train_y, epochs=1, callbacks=callbacks, verbose=1, # Logs once per epoch. batch_size=batch_size, shuffle=True)
def fit(self, train_x, train_y, validation_data_fit, epochs, cur_model_run_loop, batch_size=64, **kwargs): index = [i for i in range(len(train_y))] np.random.shuffle(index) X = train_x train_y = ohe2cat(train_y) Y_b = np.eye(self.num_classes)[train_y] X = X[index] Y_b = Y_b[index] if batch_size >= len(X): batch_size = int(len(X) / 2) print("TF model---------- batch_size:%d, X length: %d" % (batch_size, len(X))) train_step = tf.train.AdamOptimizer(0.0025).minimize(self.losses) init_global = tf.global_variables_initializer() init_local = tf.local_variables_initializer() saver = tf.train.Saver() with tf.Session() as sess: sess.run([init_global, init_local]) rounds = min(int(len(X) / batch_size), self.rr) print("TF model ---------- Rounds:%d" % rounds) for i in range(rounds): start = i * batch_size end = (i + 1) * batch_size _ = sess.run(train_step, feed_dict={ self.input_x: X[start:end], self.input_y: Y_b[start:end] }) self.rr += self.add_rr saver.save(sess, './ft.ckpt') return 0
def fit(self, x_train, y_train, *args, **kwargs): # sscaler = StandardScaler() # x_train = sscaler.fit_transform(x_train[:, :]) self._existed_classes = set(ohe2cat(y_train)) print("=== svm class {}".format(len(self._existed_classes))) self._model.fit(x_train, ohe2cat(y_train))
def fit(self, x_train, y_train, *args, **kwargs): print("=== lr fit {}".format(y_train.shape)) self._existed_classes = set(ohe2cat(y_train)) print("=== lr class {}".format(len(self._existed_classes))) self._model.fit(x_train, ohe2cat(y_train))
def fit(self, x_train, y_train, *args, **kwargs): if not self._is_multilabel: self._model.fit(x_train, ohe2cat(y_train)) else: self._model.fit(x_train, y_train)