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)