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q2_neural.py
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q2_neural.py
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import numpy as np
import random
from q1_softmax import softmax
from q2_sigmoid import sigmoid, sigmoid_grad
from q2_gradcheck import gradcheck_naive
def forward_backward_prop(data, labels, params, dimensions):
"""
Forward and backward propagation for a two-layer sigmoidal network
Compute the forward propagation and for the cross entropy cost,
and backward propagation for the gradients for all parameters.
"""
### Unpack network parameters (do not modify)
ofs = 0
Dx, H, Dy = (dimensions[0], dimensions[1], dimensions[2])
W1 = np.reshape(params[ofs:ofs+ Dx * H], (Dx, H))
ofs += Dx * H
b1 = np.reshape(params[ofs:ofs + H], (1, H))
ofs += H
W2 = np.reshape(params[ofs:ofs + H * Dy], (H, Dy))
ofs += H * Dy
b2 = np.reshape(params[ofs:ofs + Dy], (1, Dy))
### YOUR CODE HERE: forward propagation
hidden_out = sigmoid(np.matmul(data,W1)+b1)
output = softmax(np.matmul(hidden_out,W2)+b2)
cost = np.sum(-labels*np.log(output))/data.shape[0]
### END YOUR CODE
### YOUR CODE HERE: backward propagation
grad_output = (output-labels)/data.shape[0]
gradW2 = np.dot(hidden_out.transpose(),grad_output)
gradb2 = np.sum(grad_output,axis=0)
grad_hidden = np.dot(grad_output,W2.transpose())
grad_hidden = grad_hidden*hidden_out*(1-hidden_out)
gradW1 = np.dot(data.transpose(),grad_hidden)
gradb1 = np.sum(grad_hidden,axis=0)
### END YOUR CODE
### Stack gradients (do not modify)
grad = np.concatenate((gradW1.flatten(), gradb1.flatten(),
gradW2.flatten(), gradb2.flatten()))
return cost, grad
def sanity_check():
"""
Set up fake data and parameters for the neural network, and test using
gradcheck.
"""
print ("Running sanity check...")
N = 20
dimensions = [10, 5, 10]
data = np.random.randn(N, dimensions[0]) # each row will be a datum
labels = np.zeros((N, dimensions[2]))
for i in range(N):
labels[i,random.randint(0,dimensions[2]-1)] = 1
params = np.random.randn((dimensions[0] + 1) * dimensions[1] + (
dimensions[1] + 1) * dimensions[2], )
gradcheck_naive(lambda params: forward_backward_prop(data, labels, params,
dimensions), params)
def your_sanity_checks():
"""
Use this space add any additional sanity checks by running:
python q2_neural.py
This function will not be called by the autograder, nor will
your additional tests be graded.
"""
print ("Running your sanity checks...")
### YOUR CODE HERE
#raise NotImplementedError
### END YOUR CODE
if __name__ == "__main__":
sanity_check()
your_sanity_checks()