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from sklearn.model_selection import train_test_split
import numpy as np
from neural_network.opts import activation
from neural_network.backprop import bp
from neural_network.model import Network
from neural_network.plot import loss_history_plt, save_plt
def init(X: np.array, hidden_size: int) -> dict:
"""
returns a dictionary containing randomly initialized
weights and biases to start off the neural_network
"""
return {
"W1": np.random.randn(X.shape[1], hidden_size),
"b1": np.zeros((1, hidden_size)),
"W2": np.random.randn(hidden_size, 1),
"b2": np.zeros((1, 1)),
}
def main(
X: np.array,
y: np.array,
args,
) -> None:
wb = init(X, args["hidden_size"])
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=0.3,
random_state=8675309
)
# once we have these results we should test it against
# the y_test data
results, loss_history = bp(X_train, y_train, wb, args)
final = results[args["epochs"] - 1]
func = activation[args["activation_func"]]["main"]
# initialize our final network
fm = Network(final_wb=final, activation_func=func)
# predict the x test data and compare it to y test data
pred = fm.predict(X_test)
mse = np.mean((pred - y_test) ** 2)
print(f"mean squared error: {mse}")
# plot predicted versus actual
# also plot the training loss over epochs
animated_loss_plt = loss_history_plt(loss_history)
save_plt(animated_loss_plt, "plt.svg", animated=True, fps=30)
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