def _prop(self, inputs: Inputs):
forward = [np.array(inputs.inputs)]
# do hidden layers
for ind in range(2):
forward.append(np.dot(forward[-1], self.weights[ind]))
forward[-1] = funcs.ReLU(forward[-1])
# do output layer
forward.append(funcs.sigmoid(forward[-1]))
# softmax the output
output = forward[-1]
output = funcs.softmax(output)
return output
# learn = np.array([[0], [16], [255], [121]])
# answer = np.array([1])
if ((n + 1) * batch) % 60000 != 0:
learn = np.reshape(X[(n * batch) % 60000: ((n + 1) * batch) % 60000:], (batch, row * row)).T
answer = Y[(n * batch) % 60000: ((n + 1) * batch) % 60000:]
else:
learn = np.reshape(X[(n * batch) % 60000: 60000:], (batch, row * row)).T
answer = Y[(n * batch) % 60000: 60000:]
# 中間層
midin = weight1.dot(learn) + b1
midout = funcs.ReLU(midin)
# 出力層
finin = weight2.dot(midout) + b2
finout = funcs.softmax(finin)
indexmax = finout.argmax(axis=0)
power = indexmax - answer
# print("answer")
# print(answer)
# print("indexmax")
# print(indexmax)
# print("power")
# print(power)
# クロスエントロピー
entropy = funcs.cross(finout, answer, end)
if n * batch % 60000 == 0:
print(str(n) + "回目")
def forward(self, x, y_one_hot):
self.y_one_hot = y_one_hot
self.y = softmax(x)
self.loss = cross_entropy_error(self.y, self.y_one_hot)
return self.loss
for batch_count in range(1, BATCH_SIZE + 1):
try:
trainImageList.append(next(train_image))
trainLabelList.append(int(next(train_label)[0]))
except StopIteration:
reachedEnd = True
break
if len(trainImageList) == 0:
break
Y = oneHot(trainLabelList)
X = createInputArray(trainImageList)
#now we have our input matrix X and one hot output matrix Y. run forward propogation
l1_out = funcs.vec_sig(np.dot(X, w1) + b1)
final_out = funcs.softmax(np.dot(l1_out, w2) + b2)
#get gradients
d2 = final_out - Y
d1 = l1_out * (1 - l1_out) * np.dot(d2, w2.T)
#update weights and bias with gradient descent
w2 = w2 - LEARN_RATE * np.dot(l1_out.T, d2)
b2 = b2 - LEARN_RATE * d2.sum(axis=0)
w1 = w1 - LEARN_RATE * np.dot(X.T, d1)
b1 = b1 - LEARN_RATE * d1.sum(axis=0)
#get batchLoss and update epoLoss
batchLoss = -1 * np.sum(Y * np.log(final_out))