Python tensorflow.keras.callbacks.CSVLogger() Examples

The following are 2 code examples of tensorflow.keras.callbacks.CSVLogger(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.keras.callbacks , or try the search function .
Example #1
Source File: main_IQA.py    From CNNArt with Apache License 2.0 5 votes vote down vote up 
def get_callbacks(model_file, logging_file=None, early_stopping_patience=None,
                      initial_learning_rate=0.01, lr_change_mode=None, verbosity=1):
        callbacks = list()

        # save the model
        callbacks.append(ModelCheckpoint(model_file, monitor='val_loss', save_best_only=True, mode='auto'))

        # records the basic metrics
        callbacks.append(CSVLogger(logging_file, append=True))
        return callbacks
Example #2
Source File: FcDEC.py    From DEC-DA with MIT License 4 votes vote down vote up 
def pretrain(self, x, y=None, optimizer='adam', epochs=200, batch_size=256,
                 save_dir='results/temp', verbose=1, aug_pretrain=False):
        print('Begin pretraining: ', '-' * 60)
        self.autoencoder.compile(optimizer=optimizer, loss='mse')

        csv_logger = callbacks.CSVLogger(save_dir + '/pretrain_log.csv')
        cb = [csv_logger]
        if y is not None and verbose > 0:
            class PrintACC(callbacks.Callback):
                def __init__(self, x, y):
                    self.x = x
                    self.y = y
                    super(PrintACC, self).__init__()

                def on_epoch_end(self, epoch, logs=None):
                    if int(epochs / 10) != 0 and epoch % int(epochs/10) != 0:
                        return
                    feature_model = Model(self.model.input,
                                          self.model.get_layer(index=int(len(self.model.layers) / 2)).output)
                    features = feature_model.predict(self.x)
                    km = KMeans(n_clusters=len(np.unique(self.y)), n_init=20, n_jobs=4)
                    y_pred = km.fit_predict(features)
                    print(' '*8 + '|==>  acc: %.4f,  nmi: %.4f  <==|'
                          % (metrics.acc(self.y, y_pred), metrics.nmi(self.y, y_pred)))

            cb.append(PrintACC(x, y))

        # begin pretraining
        t0 = time()
        if not aug_pretrain:
            self.autoencoder.fit(x, x, batch_size=batch_size, epochs=epochs, callbacks=cb, verbose=verbose)
        else:
            print('-=*'*20)
            print('Using augmentation for ae')
            print('-=*'*20)
            def gen(x, batch_size):
                if len(x.shape) > 2:  # image
                    gen0 = self.datagen.flow(x, shuffle=True, batch_size=batch_size)
                    while True:
                        batch_x = gen0.next()
                        yield [batch_x, batch_x]
                else:
                    width = int(np.sqrt(x.shape[-1]))
                    if width * width == x.shape[-1]:  # gray
                        im_shape = [-1, width, width, 1]
                    else:  # RGB
                        width = int(np.sqrt(x.shape[-1] / 3.0))
                        im_shape = [-1, width, width, 3]
                    gen0 = self.datagen.flow(np.reshape(x, im_shape), shuffle=True, batch_size=batch_size)
                    while True:
                        batch_x = gen0.next()
                        batch_x = np.reshape(batch_x, [batch_x.shape[0], x.shape[-1]])
                        yield [batch_x, batch_x]
            self.autoencoder.fit_generator(gen(x, batch_size), steps_per_epoch=int(x.shape[0]/batch_size),
                                           epochs=epochs, callbacks=cb, verbose=verbose,
                                           workers=8, use_multiprocessing=True if platform.system() != "Windows" else False)
        print('Pretraining time: ', time() - t0)
        self.autoencoder.save_weights(save_dir + '/ae_weights.h5')
        print('Pretrained weights are saved to %s/ae_weights.h5' % save_dir)
        self.pretrained = True
        print('End pretraining: ', '-' * 60)