Theano??????????????????????????????????????????

???Theano?????????????????????????????????????????????????????????????????????

1. ??????????????????????????????

import theano
import theano.tensor as T
import numpy as np

2. ??????????????????????????????

# ?????????????????????????????????
X = T.matrix('X')
y = T.ivector('y')

# ???????????????????????????
W = theano.shared(np.random.randn(input_size, hidden_size), name='W', borrow=True)
b = theano.shared(np.zeros(hidden_size), name='b', borrow=True)

3. ???????????????????????????

# ?????????????????????????????????
hidden_output = T.nnet.sigmoid(T.dot(X, W) + b)
output = T.nnet.softmax(T.dot(hidden_output, W_output) + b_output)
prediction = T.argmax(output, axis=1)

4. ????????????????????????????????????

# ??????????????????????????????????????????
loss = T.nnet.categorical_crossentropy(output, y).mean() + lambda_reg * (T.sum(W ** 2) + T.sum(W_output ** 2))

# ??????????????????
updates = [
    (W, W - learning_rate * T.grad(loss, W)),
    (b, b - learning_rate * T.grad(loss, b)),
    (W_output, W_output - learning_rate * T.grad(loss, W_output)),
    (b_output, b_output - learning_rate * T.grad(loss, b_output))
]

5. ??????Theano?????????

# ?????????????????????????????????
train_fn = theano.function(inputs=[X, y], outputs=loss, updates=updates)
predict_fn = theano.function(inputs=[X], outputs=prediction)

?????????????????????????????????Theano?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????