def ResNet(x, **kwarg): return ResnetBuilder.build(x, **kwarg)
samples = np.load(save_dir+"training_data_2.npz") # Splitting the data train_data = samples['images'][:40000] train_targets = samples['mags'][:40000] val_data = samples['images'][40000:80000] val_targets = samples['mags'][40000:80000] test_data = samples['images'][80000:] test_targets = samples['mags'][80000:] # Making the model and training model = ResnetBuilder.build(input_shape=(1, 32, 32), num_outputs=1, block_fn='basic_block', repetitions=[15, 20, 30, 15]) model.compile(optimizer=optimizers.Adam(lr=0.00005), loss='mse', metrics=['mse']) history = model.fit(train_data, train_targets, epochs=40, batch_size=100, validation_data=(val_data, val_targets)) model.save('magnitude_regression.h5') # Plotting a Graph mse = history.history['mean_squared_error'] val_mse = history.history['val_mean_squared_error'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(1, len(mse) + 1)