def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() sample = self.features[idx] return sample, sample if __name__ == "__main__": dataset = RepresentationDataset(nuclear_charge=1) vae_structure = VAE(dataset.features.shape[-1], layer_size=128, n_layers=2, variant=3, dimensions=2, activation=F.leaky_relu) model = Model(dataset=dataset, model=vae_structure, epochs=50, learning_rate=3e-3, batch_size=100, log_interval=100) print(3e-2) model.fit() model.set_learning_rate(1e-2) print(1e-2) model.epochs = 30 model.fit() model.set_learning_rate(3e-3) print(3e-3) model.epochs = 90 model.fit() model.set_learning_rate(1e-3) print(1e-3) model.epochs = 30 model.fit()
from data import loading_data from main import Model def image(filename): image = cv2.imread(filename, cv2.IMREAD_COLOR) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) img = cv2.resize(gray, (28, 28), interpolation=cv2.INTER_AREA) img = img.reshape(1, 28, 28, 1) img = img / 255.0 return img if __name__ == "__main__": model = Model(input_shape=(28, 28, 1), classes=10) train_ds, test_ds = loading_data() model.compile( learning_rate=0.01, optimizer='sgd', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)) model.fit(epochs=15, train_ds=train_ds, test_ds=test_ds) #filename='images/9.jpg' #img = image(filename) #pred = nn.predict(img) #final_pred = np.argmax(pred) #print(pred) #print(final_pred) model.save('digit_model.h5')