def mlp(epochs: int):
set_seed(42)
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
model = Sequential([
InputLayer(input_shape=(x_train.shape[1], )),
Dense(10, input_shape=(x_train.shape[1], ), activation="relu"),
Dense(10, activation="relu"),
Dense(10, activation="relu"),
Dense(10, activation="relu"),
Dropout(0.2),
Dense(1, activation="relu")
])
model.compile(optimizer="nadam", loss="mse")
history = model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
epochs=epochs,
batch_size=100,
shuffle=True).history
model.save("model.h5")
pd.DataFrame(history).to_csv("history.csv")
#plot_history(history)
plt.savefig("history.png")
def model(input_size: int):
"""Return a multi-layer perceptron."""
set_seed(42)
model = Sequential([
InputLayer(input_shape=(input_size, )),
*[Dense(80, activation="relu") for _ in range(2)],
Dropout(0.2),
Dense(1, activation="sigmoid")
])
model.compile(optimizer="nadam",
loss='binary_crossentropy',
metrics=["auprc", "auroc", "accuracy"])
return model
def get_batch_sizes(resolution:int, minimum:int, size:int, seed:int, base:float=1.1, delta:int=10):
set_seed(seed)
batch_sizes = base**np.arange(delta, delta+resolution)
np.random.shuffle(batch_sizes)
return minimum+np.ceil(batch_sizes/np.max(batch_sizes)*(size-minimum)).astype(int)
def test_set_seed():
set_seed(42, True)