encoded_x = Onehot_encoder.fit_transform(all_data)
x_train, x_test, y_train, y_test = train_test_split(encoded_x,
y,
test_size=0.15,
random_state=21)
def accuracy(y, y_hat):
y_hat = (y_hat >= 0.5).astype('int')
y = y.astype('int')
return np.mean(y_hat[:, 0] == y)
model = Model()
model.add_layer(Layer(965, 10, tanh))
model.add_layer(Layer(10, 10, tanh))
model.add_layer(Layer(10, 10, tanh))
model.add_layer(Layer(10, 10, tanh))
model.add_layer(Layer(10, 1, sigmoid))
model.compile(BinaryCrossEntropyLoss,
DataLoader,
accuracy,
batches_per_epoch=20,
n_workers=10)
print(x_train.shape, y_train.shape, y_train.shape, y_test.shape)
index_list, cost_list = model.fit(x_train, y_train, 500)
y_hat = model.predict(x_test)
#print(confusion_matrix(y_test, y_hat))
split = int(0.8 * all_data.shape[0])
x_train = all_data[:split, 1:]
x_test = all_data[split:, 1:]
y_train = all_data[:split, 0]
y_test = all_data[split:, 0]
y_train = one_hot(y_train.astype('int'))
y_test = one_hot(y_test.astype('int'))
def accuracy(y, y_hat):
y = np.argmax(y, axis=1)
y_hat = np.argmax(y_hat, axis=1)
return np.mean(y==y_hat)
def relu(x):
return np.maximum(x, 0)
model = Model()
model.add_layer(Layer(784, 10, softmax))
#model.add_layer(Layer(64, 64, relu))
#model.add_layer(Layer(64, 10, softmax))
model.compile(CrossEntropyLoss, DataLoader, accuracy,
batches_per_epoch=x_train.shape[0] // 32 + 1,
n_workers=50, c1=1., c2=2.)
model.fit(x_train, y_train, 100)
y_hat = model.predict(x_test)
print('Accuracy on test:', accuracy(y_test, y_hat))
x_test = all_data[split:, 0:-1]
y_train = all_data[:split, -1]
y_test = all_data[split:, -1]
def one_hot(y, depth=10):
y_1hot = np.zeros((y.shape[0], depth))
y_1hot[np.arange(y.shape[0]), y] = 1
return y_1hot
y_train = one_hot(y_train.astype('int'), depth=2)
y_test = one_hot(y_test.astype('int'), depth=2)
def accuracy(y, y_hat):
y = np.argmax(y, axis=1)
y_hat = np.argmax(y_hat, axis=1)
return np.mean(y==y_hat)
model = Model()
model.add_layer(Layer(30, 7, relu)))
model.add_layer(Layer(7, 2, softmax))
model.compile(CrossEntropyLoss, DataLoader, accuracy, batches_per_epoch=(x_train.shape[0]//16)+1, n_workers=12)
model.fit(X=x_train, y=y_train, epochs=100)
y_hat = model.predict(x_test)
print('Accuracy on test:', accuracy(y_test, y_hat))
elaped_time = time.process_time() - t
print("Elapsed Time:", elaped_time)
