y_train,
test_size=0.15,
random_state=24)
print(y_train)
network = NeuralNetwork(
layer_dimensions=[X_train.shape[1], 32, 16, y_train.shape[1]],
activations=[Sigmoid, Sigmoid, Sigmoid],
keep_prob=[1.0, 0.8, 0.9, 1.0],
# keep_prob=[1.0, 0.8, 0.7, 1.0],
he_initialization=True)
print(X.shape, y.shape)
network.draw(filename='mnist', format='png')
network.fit(X_train.T,
y_train.T,
X_val.T,
y_val.T,
learning_rate=0.1,
epochs=25000,
verbose=100)
y_val_pred = np.argmax(network.predict(X_val.T), axis=0)
cm = confusion_matrix(np.argmax(y_val.T, axis=0),
y_val_pred,
labels=list(range(10)))
print(cm)
import matplotlib.pylab as plt
import numpy as np
from activation_functions import Sigmoid, ReLU, Swich
from nn import NeuralNetwork
network = NeuralNetwork(
layer_dimensions=[2, 2, 1],
activations=[Sigmoid, Sigmoid],
keep_prob=[1.0, 1.0, 1.0],
he_initialization=True,
)
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]).T
y = np.array([[0], [1], [1], [0]]).T
network.draw(filename='xor', view=False, format='png')
network.fit(x, y, learning_rate=0.3, epochs=10000, verbose=100)
print(network.predict(x))
plt.plot(network.cost, label='loss')
plt.plot(network.acc, label='acc')
plt.legend()
plt.show()
