# [0.175430, 0.790112, -0.267367],
# [-0.732674, -0.825474, 0.232357]]], bias=True)
# ]])
nn.setActivations(['relu', 'linear'])
nn.setVerbose([])
nn.checkup(inputData, targetData)
verbosePrint.vIteration = -1
verbosePrint.stage = ''
cycles = 1000
report = cycles / 10
for iteration in range(cycles + 1):
vprint(iteration, '~~~~~~~~~~~ Iteration %d ~~~~~~~~~~~' % iteration)
combinedError = 0
for row_index in range(len(targetTraining)):
datain = inputTraining[row_index:row_index + 1]
goal_prediction = targetTraining[row_index:row_index + 1]
prediction = nn.fire(datain)
# print('Prediction:' + str(prediction))
vprint(iteration, nn)
error = (goal_prediction - prediction)**2
combinedError += error
nn.learn(datain, goal_prediction)
if iteration % report == 0:
print('Iteration: %d Error: %s' % (iteration, str(combinedError)))
nn.learn(datain, goal)
if demo == 6:
nn = NNet([[[0.5], [0.48], [-0.7]]])
nn.setAlpha(0.1)
streetlights = np.array([[1, 0, 1], [0, 1, 1], [0, 0, 1], [1, 1, 1],
[0, 1, 1], [1, 0, 1]])
walk_vs_stop = np.array([[0, 1, 0, 1, 1, 0]]).T
datain = streetlights[0] # [1,0,1]
goal_prediction = walk_vs_stop[0] # equals 0... i.e. "stop"
for iteration in range(40):
vprint(iteration, '~~~~~~~~~~~ Iteration %d ~~~~~~~~~~~' % iteration)
error_for_all_lights = 0
for row_index in range(len(walk_vs_stop)):
datain = streetlights[row_index:row_index + 1]
goal_prediction = walk_vs_stop[row_index:row_index + 1]
prediction = nn.fire(datain)
# print('Prediction:' + str(prediction))
vprint(iteration, nn)
error = (goal_prediction - prediction)**2
error_for_all_lights += error
nn.learn(datain, goal_prediction)
print("Error:" + str(error_for_all_lights))
vprint(iteration, '')
[0, 0, 0],
]
nn = NNet(sizes=[5, 3], bias=True)
nn.setActivations(['sigmoid'])
verbosePrint.vIteration = -1
verbosePrint.stage = ''
nn.checkup(inputData, targetData)
cycles = 40
report = cycles/10
for iteration in range(cycles + 1):
vprint(iteration, '~~~~~~~~~~~ Iteration %d ~~~~~~~~~~~' % iteration)
combinedError = 0
for row_index in range(len(targetData)):
datain = inputData[row_index:row_index + 1]
goal_prediction = targetData[row_index:row_index + 1]
prediction = nn.fire(datain)
# print('Prediction:' + str(prediction))
vprint(iteration, 'Goal:\n%s' % str(goal_prediction))
vprint(iteration, nn)
error = (goal_prediction - prediction) ** 2
combinedError += error
nn.learn(datain, goal_prediction)
if iteration % report == 0:
layer_0 = images[i:i + 1]
layer_1 = relu(np.dot(layer_0, weights_0_1))
layer_2 = np.dot(layer_1, weights_1_2)
error += np.sum((labels[i:i + 1] - layer_2) ** 2)
correct_cnt += int(np.argmax(layer_2) == \
np.argmax(labels[i:i + 1]))
layer_2_delta = (labels[i:i + 1] - layer_2)
deriv = relu2deriv(layer_1)
layer_1_delta = layer_2_delta.dot(weights_1_2.T) \
* deriv
l1_dot_l2d = layer_1.T.dot(layer_2_delta)
weights_1_2 += alpha * l1_dot_l2d
l0_dot_l1d = layer_0.T.dot(layer_1_delta)
vprint(i, 'L0 * L1d: %s' % l0_dot_l1d,
quit=True, suffix='a')
weights_0_1 += alpha * l0_dot_l1d
vprintnn(i, quit=True, suffix='b')
sys.stdout.write("\r I:" + str(j) + \
" Train-Err:" + str(error / float(len(images)))[0:5] + \
" Train-Acc:" + str(correct_cnt / float(len(images))))
if (j % 10 == 0 or j == iterations - 1):
error, correct_cnt = (0.0, 0)
for i in range(len(test_images)):
layer_0 = test_images[i:i + 1]
layer_1 = relu(np.dot(layer_0, weights_0_1))
layer_2 = np.dot(layer_1, weights_1_2)