import numpy as np
import Layers
train_x = np.random.uniform(-5, 5, (100, 10))
ans_w = np.random.uniform(-5, 5, (10, 1))
ans_b = 3
train_y = train_x.dot(ans_w) + ans_b
w = np.random.uniform(-5, 5, (10, 1))
b = np.random.uniform(-5, 5, (1, 1))
learning_rate = 0.001
epoch = 20000
keys = ['w', 'b']
layers = {}
layers['w'] = Layers.MulLayer(w)
layers['b'] = Layers.AddLayer(b)
lastlayer = Layers.MSELayer(train_y)
grads = {}
for epoch in range(epoch):
x = train_x
y = train_y
for key in keys:
x = layers[key].forward(x)
loss = lastlayer.forward(x)
if epoch % 100 == 0:
print("err : ", loss)
dout = lastlayer.backward()
y_train = one_hot
# initialize parameters
num_classes = y_train.shape[1]
hidden = 50
keys = ['w1', 'b1', 'ReLU', 'w2', 'b2']
params = {}
params['w1'] = np.random.uniform(-1, 1, (x_train.shape[1], hidden))
params['b1'] = np.random.uniform(-1, 1, (hidden))
params['w2'] = np.random.uniform(-1, 1, (hidden, num_classes))
params['b2'] = np.random.uniform(-1, 1, (num_classes))
layers = {}
layers['w1'] = Layers.MulLayer(params['w1'])
layers['b1'] = Layers.AddLayer(params['b1'])
layers['ReLU'] = Layers.ELU()
layers['w2'] = Layers.MulLayer(params['w2'])
layers['b2'] = Layers.AddLayer(params['b2'])
lastlayer = Layers.SoftmaxLayer()
grads = {}
# initialize hyperparameters
learning_rate = 0.01
epochs = 10000
batch_size = 512
for epoch in range(epochs):
batch_mask = np.random.choice(x_train.shape[0], batch_size)
x = x_train[batch_mask]
y_test = one_hot[50000:60000]
x_test = x_train[50000:60000]
x_train = x_train[:50000]
# initialize parameters
num_classes = y_train.shape[1]
hidden = 50
x_train = x_train.reshape(x_train.shape[0], 1, 28, 28)
x_test = x_test.reshape(x_test.shape[0], 1, 28, 28)
print(x_train.shape)
model = Model()
model.addlayer(ConvLayer(32, (3, 3)), name='conv1')
model.addlayer(Layers.ReLU(), name='relu1')
model.addlayer(Layers.Dropout(), name='dropout1')
model.addlayer(Layers.Flatten(), name='flatten')
model.addlayer(Layers.MulLayer(10), name="w1")
model.addlayer(Layers.AddLayer(10), name='b1')
model.addlayer(Layers.ReLU(), name='relu3')
model.addlayer(Layers.Dropout(0.5), name='dropout3')
model.addlayer(Layers.SoftmaxLayer(), name='softmax')
optimizer = Optimizer.Adam(batch_size=32)
model.train(x_train, y_train, optimizer, 10000, 0.01)
model.save()
print("--TRAIN EVAL--")
model.eval(x_train, y_train)
print("--TEST EVAL--")
model.eval(x_test, y_test)
