def sanity_layers(data):
"""
Check implementation of the forward and backward pass for all activations.
"""
# Set the seed to reproduce results.
np.random.seed(42)
# Pseudo-input.
random_input = np.random.randn(1, 50)
# Get the activations.
act_sigmoid = neuralnet.Activation('sigmoid')
act_tanh = neuralnet.Activation('tanh')
act_ReLU = neuralnet.Activation('ReLU')
act_leakyReLU = neuralnet.Activation('leakyReLU')
# Get the outputs for forward-pass.
out_sigmoid = act_sigmoid(random_input)
out_tanh = act_tanh(random_input)
out_ReLU = act_ReLU(random_input)
out_leakyReLU = act_leakyReLU(random_input)
# Compute the errors.
err_sigmoid = np.sum(np.abs(data['out_sigmoid'] - out_sigmoid))
err_tanh = np.sum(np.abs(data['out_tanh'] - out_tanh))
err_ReLU = np.sum(np.abs(data['out_ReLU'] - out_ReLU))
err_leakyReLU = np.sum(np.abs(data['out_leakyReLU'] - out_leakyReLU))
# Check the errors.
check_error(err_sigmoid, "Sigmoid Forward Pass")
check_error(err_tanh, "Tanh Forward Pass")
check_error(err_ReLU, "ReLU Forward Pass")
check_error(err_leakyReLU, "leakyReLU Forward Pass")
print(20 * "-", "\n")
# Compute the gradients.
grad_sigmoid = act_sigmoid.backward(1.0, 1)
grad_tanh = act_tanh.backward(1.0, 1)
grad_ReLU = act_ReLU.backward(1.0, 1)
grad_leakyReLU = act_leakyReLU.backward(1.0, 1)
# Compute the errors.
err_sigmoid_grad = np.sum(np.abs(data['grad_sigmoid'] - grad_sigmoid))
err_tanh_grad = np.sum(np.abs(data['grad_tanh'] - grad_tanh))
err_ReLU_grad = np.sum(np.abs(data['grad_ReLU'] - grad_ReLU))
err_leakyReLU_grad = np.sum(np.abs(data['grad_leakyReLU'] -
grad_leakyReLU))
# Check the errors.
check_error(err_sigmoid_grad, "Sigmoid Gradient")
check_error(err_tanh_grad, "Tanh Gradient")
check_error(err_ReLU_grad, "ReLU Gradient")
check_error(err_leakyReLU_grad, "leakyReLU Gradient")
print(20 * "-", "\n")
def main():
# make_pickle()
benchmark_data = pickle.load(open('validate_data.pkl', 'rb'),
encoding='latin1')
config = {}
config['layer_specs'] = [
784, 100, 100, 10
] # The length of list denotes number of hidden layers; each element denotes number of neurons in that layer; first element is the size of input layer, last element is the size of output layer.
config[
'activation'] = 'sigmoid' # Takes values 'sigmoid', 'tanh' or 'ReLU'; denotes activation function for hidden layers
config[
'batch_size'] = 1000 # Number of training samples per batch to be passed to network
config['epochs'] = 50 # Number of epochs to train the model
config['early_stop'] = True # Implement early stopping or not
config[
'early_stop_epoch'] = 5 # Number of epochs for which validation loss increases to be counted as overfitting
config['L2_penalty'] = 0 # Regularization constant
config['momentum'] = False # Denotes if momentum is to be applied or not
config[
'momentum_gamma'] = 0.9 # Denotes the constant 'gamma' in momentum expression
np.random.seed(42)
x = np.random.randn(1, 100)
act_sigmoid = neuralnet.Activation('sigmoid')
act_tanh = neuralnet.Activation('tanh')
act_ReLU = neuralnet.Activation('ReLU')
out_sigmoid = act_sigmoid.forward_pass(x)
err_sigmoid = np.sum(np.abs(benchmark_data['out_sigmoid'] - out_sigmoid))
check_error(err_sigmoid, "Sigmoid Forward Pass")
out_tanh = act_tanh.forward_pass(x)
err_tanh = np.sum(np.abs(benchmark_data['out_tanh'] - out_tanh))
check_error(err_tanh, "Tanh Forward Pass")
out_ReLU = act_ReLU.forward_pass(x)
err_ReLU = np.sum(np.abs(benchmark_data['out_ReLU'] - out_ReLU))
check_error(err_ReLU, "ReLU Forward Pass")
print("**************")
grad_sigmoid = act_sigmoid.backward_pass(1.0)
err_sigmoid_grad = np.sum(
np.abs(benchmark_data['grad_sigmoid'] - grad_sigmoid))
check_error(err_sigmoid_grad, "Sigmoid Gradient")
grad_tanh = act_tanh.backward_pass(1.0)
err_tanh_grad = np.sum(np.abs(benchmark_data['grad_tanh'] - grad_tanh))
check_error(err_tanh_grad, "Tanh Gradient")
grad_ReLU = act_ReLU.backward_pass(1.0)
err_ReLU_grad = np.sum(np.abs(benchmark_data['grad_ReLU'] - grad_ReLU))
check_error(err_ReLU_grad, "ReLU Gradient")
np.random.seed(42)
x_image = np.random.randn(1, 784)
nnet = neuralnet.Neuralnetwork(config)
nnet.forward_pass(x_image,
targets=np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0]))
nnet.backward_pass()
layer_no = 0
for layer_idx, layer in enumerate(nnet.layers):
if isinstance(layer, neuralnet.Layer):
layer_no += 1
error_x = np.sum(
np.abs(benchmark_data['nnet'].layers[layer_idx].x - layer.x))
error_w = np.sum(
np.abs(benchmark_data['nnet'].layers[layer_idx].w - layer.w))
error_b = np.sum(
np.abs(benchmark_data['nnet'].layers[layer_idx].b - layer.b))
error_d_w = np.sum(
np.abs(benchmark_data['nnet'].layers[layer_idx].d_w -
layer.d_w))
error_d_b = np.sum(
np.abs(benchmark_data['nnet'].layers[layer_idx].d_b -
layer.d_b))
check_error(error_x, "Layer{} Input".format(layer_no))
check_error(error_w, "Layer{} Weights".format(layer_no))
check_error(error_b, "Layer{} Biases".format(layer_no))
check_error(error_d_w, "Layer{} Weight Gradient".format(layer_no))
check_error(error_d_b, "Layer{} Bias Gradient".format(layer_no))
def main():
# make_pickle()
benchmark_data = pickle.load(open('validate_data.pkl', 'rb'),
encoding='latin1')
config = {}
config['layer_specs'] = [
784, 50, 10
] # The length of list denotes number of hidden layers; each element denotes number of neurons in that layer; first element is the size of input layer, last element is the size of output layer.
config[
'activation'] = 'tanh' # Takes values 'sigmoid', 'tanh' or 'ReLU'; denotes activation function for hidden layers
input_size = config['layer_specs'][0]
hidden_size = config['layer_specs'][1]
output_size = config['layer_specs'][2]
np.random.seed(42)
w1 = np.random.randn(input_size, hidden_size) # Weight matrix 1
b1 = np.random.randn(1, 1).dot(
np.ones((1, hidden_size)).astype(np.float32)) # Bias vector 1
w2 = np.random.randn(hidden_size, output_size) # Weight matrix 2
b2 = np.random.randn(1, 1).dot(
np.ones((1, output_size)).astype(np.float32)) # Bias vector 2
X_test, y_test = neuralnet.load_data('MNIST_test.pkl') # test data
y_test_onehot = [[1 if i == y else 0 for i in range(10)] for y in y_test]
act = neuralnet.Activation('tanh')
epsilon = 0.01 # epsilon value
x_index, y_index = 0, 3
op = 0 # 0:input to hidden weight; 1:hidden to output weight; 2:hidden bias weight; 3:hidden bias weight
ops = [[epsilon, 0, 0, 0], [0, epsilon, 0, 0], [0, 0, epsilon, 0],
[0, 0, 0, epsilon]]
w1[x_index][y_index] += ops[op][0] # increase the weight by epsilon
w2[x_index][y_index] += ops[op][1]
for b in b1:
b += ops[op][2]
for b in b2:
b += ops[op][3]
input_h = np.dot(X_test[0], w1) + b1
output_h = act.sigmoid(input_h)
input_o = np.dot(output_h, w2) + b2
output_o = act.sigmoid(input_o)
loss1 = loss_func(output_o, y_test_onehot[0])
w1[x_index][y_index] -= 2 * ops[op][0] # decrease the weight by 2 epsilon
w2[x_index][y_index] -= 2 * ops[op][1]
for b in b1:
b -= 2 * ops[op][2]
for b in b2:
b -= 2 * ops[op][3]
input_h = np.dot(X_test[0], w1) + b1
output_h = act.sigmoid(input_h)
input_o = np.dot(output_h, w2) + b2
output_o = act.sigmoid(input_o)
loss2 = loss_func(output_o, y_test_onehot[0])
diff1 = (loss1 - loss2) / (2 * epsilon)
w1[x_index][y_index] += ops[op][0] # recover the weight
w2[x_index][y_index] += ops[op][1]
for b in b1:
b += ops[op][2]
for b in b2:
b += ops[op][3]
input_h = np.dot(X_test[0], w1) + b1
output_h = act.sigmoid(input_h)
input_o = np.dot(output_h, w2) + b2
output_o = act.sigmoid(input_o)
# choose hidden weight
if op == 0:
ans = 0
for l in range(10):
ans += (w2[y_index][l] * (output_o[0][l] - y_test_onehot[0][l]) *
output_o[0][l] * (1 - output_o[0][l]))
ans *= (output_h[0][y_index] * (1 - output_h[0][y_index]) *
X_test[0][x_index])
diff2 = ans
# choose output weight
elif op == 1:
diff2 = (output_o[0][y_index] -
y_test_onehot[0][y_index]) * output_o[0][y_index] * (
1 - output_o[0][y_index]) * output_h[0][x_index]
# choose input bias weight
elif op == 2:
diff2 = 0
for i in range(50):
ans = 0
for l in range(10):
ans += (w2[i][l] * (output_o[0][l] - y_test_onehot[0][l]) *
output_o[0][l] * (1 - output_o[0][l]))
ans *= output_h[0][i] * (1 - output_h[0][i])
diff2 += ans
# choose hidden bias weight
else:
diff2 = 0
for i in range(10):
diff2 += (output_o[0][i] - y_test_onehot[0][i]
) * output_o[0][i] * (1 - output_o[0][i])
print(abs(diff1), abs(diff2))
print("difference :", abs(diff1 - diff2))
