demo-tensorflow

from dataset import generate_data
import numpy as np

import os

from model import enhancednet

os.environ["CUDA_VISIBLE_DEVICES"] = "0"

image_rows = 128
image_cols = 256
filename = 'detached_data.mat'
train_data, train1_data, label_data = generate_data(filename)
train_data = np.array(train_data, dtype=float)
train_data = np.reshape(train_data, (len(train_data), image_rows, image_cols, 1))
# print(train_data.shape)
train1_data = np.array(train1_data, dtype=float)
train1_data = np.reshape(train1_data, (len(train1_data), image_rows, image_cols, 1))

model = enhancednet()
model.fit(train_data, train1_data, batch_size=32, epochs=100, verbose=2, shuffle=True, validation_split=0.1)

model.save('enhanced_model.h5')