from HAL_9000.activation_functions import Sigmoid, Softmax
from HAL_9000.loss_functions import CrossEntropy
from HAL_9000.utils import train_test_split, accuracy_score, normalize, to_categorical
data = datasets.load_digits()
X = data.data
y = data.target
# print(X.shape)
# print(y[20])
# plt.imshow(X[20].reshape((8, 8)), cmap='gray')
# plt.show()
X = normalize(X)
y = to_categorical(y)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=1)
model = HAL_9000.MLP(n_hidden=10,
n_iter=2000,
lr=1e-3,
hid_afn=Sigmoid,
out_afn=Softmax)
model.fit(X_train, y_train)
prediction = model.predict(X_test)
y_pred = np.argmax(prediction, axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = accuracy_score(y_test, y_pred)
print(accuracy)
import HAL_9000
from HAL_9000.optimizers import Adam
from HAL_9000.loss_functions import CrossEntropy
from HAL_9000.activation_functions import Softmax
from HAL_9000.brain_layers import Dense, Activation
from HAL_9000.utils import train_test_split, normalize, to_categorical, accuracy_score
data = datasets.load_digits()
X = data.data
X = normalize(X)
n_samples, n_features = np.shape(X)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
brain = HAL_9000.Brain(loss=CrossEntropy, opt=Adam())
brain.add(Dense(input_shape=(n_features,), n_units=64))
brain.add(Activation('relu'))
brain.add(Dense(n_units=64))
brain.add(Activation('relu'))
brain.add(Dense(n_units=10))
brain.add(Activation('softmax'))
brain.summary()
t_l, t_a, _, _ = brain.fit(X_train, to_categorical(
y_train), epochs=50, batch_size=50)
y_pred = np.argmax(brain.predict(X_test), axis=1)
acc = accuracy_score(y_test, y_pred)