def train(batch_size = 32, save_path = 'chk_classifier.hdf5', resume_flag = False): checkpointer = ModelCheckpoint(monitor = 'val_acc', filepath = save_path, verbose = 1, save_best_only = True, save_weights_only = True) early_stopper = EarlyStopping(monitor = 'val_loss', min_delta = 0.001, patience = 20) lr_reducer = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.8, verbose = 1, patience = 4, min_lr = 2E-6) data_loader = DataLoader(data_path = 'data') data_loader.load_data() model = Classifier(data_loader.max_feature_value, data_loader.max_sequence_len, num_classes = len(data_loader.classes)) classifier = model() classifier.summary() train_steps = data_loader.train_size // batch_size val_steps = data_loader.val_size // batch_size if resume_flag and os.path.exists(save_path): classifier.load_weights(save_path) classifier.fit_generator(generator = data_loader.generate(mode = 'train'), steps_per_epoch = train_steps, epochs = 350, verbose = 1, validation_data = data_loader.generate(mode = 'val'), validation_steps = val_steps, callbacks = [checkpointer, early_stopper, lr_reducer])
def test(batch_size = 1, save_path = 'chk_classifier.hdf5'):
data_loader = DataLoader(data_path = 'data')
data_loader.load_data()
model = Classifier(data_loader.max_feature_value,
data_loader.max_sequence_len,
num_classes = len(data_loader.classes))
classifier = model()
classifier.load_weights(save_path)
generator = data_loader.generate(batch_size = batch_size, mode = 'val')
x, y = next(generator)
pred = classifier.predict(x)
y_in = data_loader.decode_label(y)
y_out = data_loader.decode_label(pred)
[print('Ground Truth: {} Prediction: {}'.format(y1, y2)) for y1, y2 in zip(y_in, y_out)]
metrics = classifier.evaluate(x, y, verbose = 1)
[print('{}: {}\n'.format(x, y)) for x, y in zip(classifier.metrics_names, metrics)]
print(metrics)
from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense import numpy as np import matplotlib import matplotlib.pyplot as plt import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Import Dataset from data_loader import DataLoader data = DataLoader(64, 64) x_test, y_test = data.get_flatten_data() print(x_test.shape) x_train, y_train = data.generate(10000, 64, 64) print(x_train.shape) # Training Parameters learning_rate = 0.0001 num_epochs = 100 # Network Parameters WIDTH = data.WIDTH HEIGHT = data.HEIGHT CHANNELS = data.CHANNELS NUM_INPUTS = WIDTH * HEIGHT * CHANNELS NUM_OUTPUTS = WIDTH * HEIGHT * CHANNELS x_train = x_test.reshape((-1, NUM_INPUTS)) y_train = y_test.reshape((-1, NUM_INPUTS))
