I wrote a RNN with LSTM cell with Pycharm. The peculiarity of this network is that the output of the RNN is fed into a integration opeartion, computed with Runge-kutta.
The integration takes some input and propagate that in time one step ahead. In order to do so I need to slice the feature tensor X along the batch dimension, and pass this to the Runge-kutta.
class MyLSTM(torch.nn.Module):
def __init__(self, ni, no, sampling_interval, nh=10, nlayers=1):
super(MyLSTM, self).__init__()
self.device = torch.device("cpu")
self.dtype = torch.float
self.ni = ni
self.no = no
self.nh = nh
self.nlayers = nlayers
self.lstms = torch.nn.ModuleList(
[torch.nn.LSTMCell(self.ni, self.nh)] + [torch.nn.LSTMCell(self.nh, self.nh) for i in range(nlayers - 1)])
self.out = torch.nn.Linear(self.nh, self.no)
self.do = torch.nn.Dropout(p=0.2)
self.actfn = torch.nn.Sigmoid()
self.sampling_interval = sampling_interval
self.scaler_states = None
# Options
# description of the whole block
def forward(self, x, h0, train=False, integrate_ode=True):
x0 = x.clone().requires_grad_(True)
hs = x # initiate hidden state
if h0 is None:
h = torch.zeros(hs.shape[0], self.nh, device=self.device)
c = torch.zeros(hs.shape[0], self.nh, device=self.device)
else:
(h, c) = h0
# LSTM cells
for i in range(self.nlayers):
h, c = self.lstms[i](hs, (h, c))
if train:
hs = self.do(h)
else:
hs = h
# Output layer
# y = self.actfn(self.out(hs))
y = self.out(hs)
if integrate_ode:
p = y
y = self.integrate(x0, p)
return y, (h, c)
def integrate(self, x0, p):
# RK4 steps per interval
M = 4
DT = self.sampling_interval / M
X = x0
# X = self.scaler_features.inverse_transform(x0)
for b in range(X.shape[0]):
xx = X[b, :]
for j in range(M):
k1 = self.ode(xx, p[b, :])
k2 = self.ode(xx + DT / 2 * k1, p[b, :])
k3 = self.ode(xx + DT / 2 * k2, p[b, :])
k4 = self.ode(xx + DT * k3, p[b, :])
xx = xx + DT / 6 * (k1 + 2 * k2 + 2 * k3 + k4)
X_all[b, :] = xx
return X_all
def ode(self, x0, y):
# Here I a dynamic model
I get this error:
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor []], which is output 0 of SelectBackward, is at version 64; expected version 63 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
the problem is in the operations xx = X[b, :] and p[b,:]. I know that because I choose batch dimension of 1, then I can replace the previous two equations with xx=X and p, and this works. How can split the tensor without loosing the gradient?
I had the same question, and after a lot of searching, I added .detach() function after "h" and "c" in the RNN cell.
Related
I have created a very simple neural network to solve the following ODE:
My code doesn't seem to work well, which is pretty weird since the ODE is simple. Below is my code with comments. I believe the problem is coming from torch.gradient(preds) but I can't figure it out. Any help would be greatly appreciated!
inputs = np.linspace(0,2,50,dtype='float32')
inputs = torch.from_numpy(inputs)
inputs.requires_grad = True
inputs.float()
# Initial Condition
A = 0.0
# Known trial solution for general ODE
trial_solution = lambda I: A + I * NN(I)
ODE = lambda x: x
# Numbers of neurons from layers 1 to 3
neuron_1 = 9
neuron_2 = 12
neuron_3 = 8
# Initializing the weights and biases
W1 = torch.randn(len(inputs), neuron_1, requires_grad=True).float()
B1 = torch.randn(neuron_1, requires_grad=True).float()
W2 = torch.randn(neuron_1, neuron_2, requires_grad=True)
B2 = torch.randn(neuron_2, requires_grad=True)
W3 = torch.randn(neuron_2, neuron_3, requires_grad=True)
B3 = torch.randn(neuron_3, requires_grad=True)
W4 = torch.randn(neuron_3, len(inputs), requires_grad=True)
B4 = torch.randn(len(inputs), requires_grad=True)
# General sigmoid function used for neurons inside hidden layers
def Sigmoid(z):
return 1/(1+torch.exp(-z))
# Defining the structure of my Neural Network
def NN(x):
z1 = W1.t() # x + B1
z2 = Sigmoid(z1)
z3 = W2.t() # z2 + B2
z4 = Sigmoid(z3)
z5 = W3.t() # z4 + B3
z6 = Sigmoid(z5)
return W4.t() # z6 + B4
# Defining the mean squared error to be my loss function
def mse(t1,t2):
diff = t1-t2
return torch.sum(diff*diff)/diff.numel()
# Iterating over number of epochs for backprop.
for i in range(10000):
preds = trial_solution(inputs)
preds_gradient = torch.gradient(preds) # Possible problem!!
preds_gradient =preds_gradient[0]
target_gradient = ODE(inputs)
loss = mse(preds_gradient,target_gradient)
loss.backward()
#print(loss)
with torch.no_grad():
W1 -= W1.grad*1e-2
B1 -= B1.grad*1e-2
W1.grad.zero_()
B1.grad.zero_()
W2 -= W2.grad*1e-2
B2 -= B2.grad*1e-2
W2.grad.zero_()
B2.grad.zero_()
W3 -= W3.grad*1e-2
B3 -= B3.grad*1e-2
W3.grad.zero_()
B3.grad.zero_()
W4 -= W4.grad*1e-2
B4 -= B4.grad*1e-2
W4.grad.zero_()
B4.grad.zero_()
numpy_tensors = np.linspace(0,2,50,dtype='float32')
numpy_tensors = torch.from_numpy(numpy_tensors)
final_pred = trial_solution(inputs).detach().numpy()
xx = np.linspace(0,2,50)
yt = xx*xx * 0.5
plt.plot(numpy_tensors,final_pred, label='Prediction from NN')
plt.plot(xx,yt,label = 'True Solution')
plt.legend()
I have hundred Entries in csv file.
Physics,Maths,Status_class0or1
30,40,0
90,70,1
Using above data i am trying to build logistic (binary) classifier.
Please advise me where i am doing wrong ? Why i am getting answer in 3*3 Matrix (9 values of theta, where as it should be 3 only)
Here is code:
importing the libraries
import numpy as np
import pandas as pd
from sklearn import preprocessing
reading data from csv file.
df = pd.read_csv("LogisticRegressionFirstBinaryClassifier.csv", header=None)
df.columns = ["Maths", "Physics", "AdmissionStatus"]
X = np.array(df[["Maths", "Physics"]])
y = np.array(df[["AdmissionStatus"]])
X = preprocessing.normalize(X)
X = np.c_[np.ones(X.shape[0]), X]
theta = np.ones((X.shape[1], 1))
print(X.shape) # (100, 3)
print(y.shape) # (100, 1)
print(theta.shape) # (3, 1)
calc_z to caculate dot product of X and theta
def calc_z(X,theta):
return np.dot(X,theta)
Sigmoid function
def sigmoid(z):
return 1 / (1 + np.exp(-z))
Cost_function
def cost_function(X, y, theta):
z = calc_z(X,theta)
h = sigmoid(z)
return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
print("cost_function =" , cost_function(X, y, theta))
def derivativeofcostfunction(X, y, theta):
z = calc_z(X,theta)
h = sigmoid(z)
calculation = np.dot((h - y).T,X)
return calculation
print("derivativeofcostfunction=", derivativeofcostfunction(X, y, theta))
def grad_desc(X, y, theta, lr=.001, converge_change=.001):
cost = cost_function(X, y, theta)
change_cost = 1
num_iter = 1
while(change_cost > converge_change):
old_cost = cost
print(theta)
print (derivativeofcostfunction(X, y, theta))
theta = theta - lr*(derivativeofcostfunction(X, y, theta))
cost = cost_function(X, y, theta)
change_cost = old_cost - cost
num_iter += 1
return theta, num_iter
Here is the output :
[[ 0.4185146 -0.56877556 0.63999433]
[15.39722864 9.73995197 11.07882445]
[12.77277463 7.93485324 9.24909626]]
[[0.33944777 0.58199037 0.52493407]
[0.02106587 0.36300629 0.30297278]
[0.07040604 0.3969297 0.33737757]]
[[-0.05856159 -0.89826735 0.30849185]
[15.18035041 9.59004868 10.92827046]
[12.4804775 7.73302024 9.04599788]]
[[0.33950634 0.58288863 0.52462558]
[0.00588552 0.35341624 0.29204451]
[0.05792556 0.38919668 0.32833157]]
[[-5.17526527e-01 -1.21534937e+00 -1.03387571e-02]
[ 1.49729502e+01 9.44663458e+00 1.07843504e+01]
[ 1.21978140e+01 7.53778010e+00 8.84964495e+00]]
(array([[ 0.34002386, 0.58410398, 0.52463592],
[-0.00908743, 0.34396961, 0.28126016],
[ 0.04572775, 0.3816589 , 0.31948193]]), 46)
I changed this code , just added Transpose while returning the matrix and it fixed my issue.
def derivativeofcostfunction(X, y, theta):
z = calc_z(X,theta)
h = sigmoid(z)
calculation = np.dot((h - y).T,X)
return calculation.T
I am learning Machine Learning course from coursera from Andrews Ng. I have written a code for logistic regression in octave. But, it is not working. Can someone help me?
I have taken the dataset from the following link:
Titanic survivors
Here is my code:
pkg load io;
[An, Tn, Ra, limits] = xlsread("~/ML/ML Practice/dataset/train_and_test2.csv", "Sheet2", "A2:H1000");
# As per CSV file we are reading columns from 1 to 7. 8-th column is Survived, which is what we are going to predict
X = [An(:, [1:7])];
Y = [An(:, 8)];
X = horzcat(ones(size(X,1), 1), X);
# Initializing theta values as zero for all
#theta = zeros(size(X,2),1);
theta = [-3;1;1;-3;1;1;1;1];
learningRate = -0.00021;
#learningRate = -0.00011;
# Step 1: Calculate Hypothesis
function g_z = estimateHypothesis(X, theta)
z = theta' * X';
z = z';
e_z = -1 * power(2.72, z);
denominator = 1.+e_z;
g_z = 1./denominator;
endfunction
# Step 2: Calculate Cost function
function cost = estimateCostFunction(hypothesis, Y)
log_1 = log(hypothesis);
log_2 = log(1.-hypothesis);
y1 = Y;
term_1 = y1.*log_1;
y2 = 1.-Y;
term_2 = y2.*log_2;
cost = term_1 + term_2;
cost = sum(cost);
# no.of.rows
m = size(Y, 1);
cost = -1 * (cost/m);
endfunction
# Step 3: Using gradient descent I am updating theta values
function updatedTheta = updateThetaValues(_X, _Y, _theta, _hypothesis, learningRate)
#s1 = _X * _theta;
#s2 = s1 - _Y;
#s3 = _X' * s2;
# no.of.rows
#m = size(_Y, 1);
#s4 = (learningRate * s3)/m;
#updatedTheta = _theta - s4;
s1 = _hypothesis - _Y;
s2 = s1 .* _X;
s3 = sum(s2);
# no.of.rows
m = size(_Y, 1);
s4 = (learningRate * s3)/m;
updatedTheta = _theta .- s4';
endfunction
costVector = [];
iterationVector = [];
for i = 1:1000
# Step 1
hypothesis = estimateHypothesis(X, theta);
#disp("hypothesis");
#disp(hypothesis);
# Step 2
cost = estimateCostFunction(hypothesis, Y);
costVector = vertcat(costVector, cost);
#disp("Cost");
#disp(cost);
# Step 3 - Updating theta values
theta = updateThetaValues(X, Y, theta, hypothesis, learningRate);
iterationVector = vertcat(iterationVector, i);
endfor
function plotGraph(iterationVector, costVector)
plot(iterationVector, costVector);
ylabel('Cost Function');
xlabel('Iteration');
endfunction
plotGraph(iterationVector, costVector);
This is the graph I am getting when I am plotting against no.of.iterations and cost function.
I am tired by adjusting theta values and learning rate. Can someone help me to solve this problem.
Thanks.
I have done a mathematical error. I should have used either power(2.72, -z) or exp(-z). Instead I have used as -1 * power(2.72, z). Now, I'm getting a proper curve.
Thanks.
I am writing this code for linear regression and trying Gradient Descent to minimize the RSS. The cost function seems to explode to infinity within 12 iterations. I know this is not supposed to happen. Maybe, I have used the wrong gradient function for RSS (can be seen in the function "grad()")?
NumberObservations=100
minVal=1
maxVal=20
X = np.random.uniform(minVal,maxVal,(NumberObservations,1))
e = np.random.normal(0, 1, (NumberObservations,1))
Y= 10 + 5*X + e
B = np.array([[0], [0]])
sum_y = sum(Y)
sum_x = sum(X)
sum_xy = sum(np.multiply(X, Y))
sum_x2 = sum(X*X)
alpha = 0.00001
iterations = 15
def cost_fun(X, Y, B):
b0 = B[0]
b1 = B[1]
s = (Y - (b0 + (b1*X)))**2
rss = sum(s)
return rss
def grad(X, Y, B):
print("B = " + str(B))
b0 = B[0]
b1 = B[1]
g0 = -2*(Y - b0 - (b1*X))
g1 = -2*((X*Y) - (b0*X) - (b1*X**2))
grad = np.concatenate((g0, g1), axis = 1)
return grad
def gradient_descent(X, Y, B, alpha, iterations):
cost_history = [0] * iterations
m = len(Y)
x0 = np.array(np.ones(m))
x0 = x0.reshape((100, 1))
X1 = np.concatenate((x0, X), axis = 1)
for iteration in range(iterations):
h = np.dot(X1, B)
h = h.reshape((100, 1))
loss = h - Y
g = grad(X, Y, B)
gradient = (np.dot(g.T, loss) / m)
B = B - alpha * gradient
cost = cost_fun(X, Y, B)
cost_history[iteration] = cost
print("Iteration %d | Cost: %f" % (iteration, cost))
print("-----------------------------------------------------------------------")
return B, cost_history
newB, cost_history = gradient_descent(X, Y, B, alpha, iterations)
# New Values of B
print(newB)
Please help.
I'm trying to implement a regression NN that has 3 layers (1 input, 1 hidden and 1 output layer with a continuous result). As a basis I took a classification NN from coursera.org class, but changed the cost function and gradient calculation so as to fit a regression problem (and not a classification one):
My nnCostFunction now is:
function [J grad] = nnCostFunctionLinear(nn_params, ...
input_layer_size, ...
hidden_layer_size, ...
num_labels, ...
X, y, lambda)
Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
hidden_layer_size, (input_layer_size + 1));
Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...
num_labels, (hidden_layer_size + 1));
m = size(X, 1);
a1 = X;
a1 = [ones(m, 1) a1];
a2 = a1 * Theta1';
a2 = [ones(m, 1) a2];
a3 = a2 * Theta2';
Y = y;
J = 1/(2*m)*sum(sum((a3 - Y).^2))
th1 = Theta1;
th1(:,1) = 0; %set bias = 0 in reg. formula
th2 = Theta2;
th2(:,1) = 0;
t1 = th1.^2;
t2 = th2.^2;
th = sum(sum(t1)) + sum(sum(t2));
th = lambda * th / (2*m);
J = J + th; %regularization
del_3 = a3 - Y;
t1 = del_3'*a2;
Theta2_grad = 2*(t1)/m + lambda*th2/m;
t1 = del_3 * Theta2;
del_2 = t1 .* a2;
del_2 = del_2(:,2:end);
t1 = del_2'*a1;
Theta1_grad = 2*(t1)/m + lambda*th1/m;
grad = [Theta1_grad(:) ; Theta2_grad(:)];
end
Then I use this func in fmincg algorithm, but in firsts iterations fmincg end it's work. I think my gradient is wrong, but I can't find the error.
Can anybody help?
If I understand correctly, your first block of code (shown below) -
m = size(X, 1);
a1 = X;
a1 = [ones(m, 1) a1];
a2 = a1 * Theta1';
a2 = [ones(m, 1) a2];
a3 = a2 * Theta2';
Y = y;
is to get the output a(3) at the output layer.
Ng's slides about NN has the below configuration to calculate a(3). It's different from what your code presents.
in the middle/output layer, you are not doing the activation function g, e.g., a sigmoid function.
In terms of the cost function J without regularization terms, Ng's slides has the below formula:
I don't understand why you can compute it using:
J = 1/(2*m)*sum(sum((a3 - Y).^2))
because you are not including the log function at all.
Mikhaill, I´ve been playing with a NN for continuous regression as well, and had a similar issues at some point. The best thing to do here would be to test gradient computation against a numerical calculation before running the model. If that´s not correct, fmincg won´t be able to train the model. (Btw, I discourage you of using numerical gradient as the time involved is much bigger).
Taking into account that you took this idea from Ng´s Coursera class, I´ll implement a possible solution for you to try using the same notation for Octave.
% Cost function without regularization.
J = 1/2/m^2*sum((a3-Y).^2);
% In case it´s needed, regularization term is added (i.e. for Training).
if (reg==true);
J=J+lambda/2/m*(sum(sum(Theta1(:,2:end).^2))+sum(sum(Theta2(:,2:end).^2)));
endif;
% Derivatives are computed for layer 2 and 3.
d3=(a3.-Y);
d2=d3*Theta2(:,2:end);
% Theta grad is computed without regularization.
Theta1_grad=(d2'*a1)./m;
Theta2_grad=(d3'*a2)./m;
% Regularization is added to grad computation.
Theta1_grad(:,2:end)=Theta1_grad(:,2:end)+(lambda/m).*Theta1(:,2:end);
Theta2_grad(:,2:end)=Theta2_grad(:,2:end)+(lambda/m).*Theta2(:,2:end);
% Unroll gradients.
grad = [Theta1_grad(:) ; Theta2_grad(:)];
Note that, since you have taken out all the sigmoid activation, the derivative calculation is quite simple and results in a simplification of the original code.
Next steps:
1. Check this code to understand if it makes sense to your problem.
2. Use gradient checking to test gradient calculation.
3. Finally, use fmincg and check you get different results.
Try to include sigmoid function to compute second layer (hidden layer) values and avoid sigmoid in calculating the target (output) value.
function [J grad] = nnCostFunction1(nnParams, ...
inputLayerSize, ...
hiddenLayerSize, ...
numLabels, ...
X, y, lambda)
Theta1 = reshape(nnParams(1:hiddenLayerSize * (inputLayerSize + 1)), ...
hiddenLayerSize, (inputLayerSize + 1));
Theta2 = reshape(nnParams((1 + (hiddenLayerSize * (inputLayerSize + 1))):end), ...
numLabels, (hiddenLayerSize + 1));
Theta1Grad = zeros(size(Theta1));
Theta2Grad = zeros(size(Theta2));
m = size(X,1);
a1 = [ones(m, 1) X]';
z2 = Theta1 * a1;
a2 = sigmoid(z2);
a2 = [ones(1, m); a2];
z3 = Theta2 * a2;
a3 = z3;
Y = y';
r1 = lambda / (2 * m) * sum(sum(Theta1(:, 2:end) .* Theta1(:, 2:end)));
r2 = lambda / (2 * m) * sum(sum(Theta2(:, 2:end) .* Theta2(:, 2:end)));
J = 1 / ( 2 * m ) * (a3 - Y) * (a3 - Y)' + r1 + r2;
delta3 = a3 - Y;
delta2 = (Theta2' * delta3) .* sigmoidGradient([ones(1, m); z2]);
delta2 = delta2(2:end, :);
Theta2Grad = 1 / m * (delta3 * a2');
Theta2Grad(:, 2:end) = Theta2Grad(:, 2:end) + lambda / m * Theta2(:, 2:end);
Theta1Grad = 1 / m * (delta2 * a1');
Theta1Grad(:, 2:end) = Theta1Grad(:, 2:end) + lambda / m * Theta1(:, 2:end);
grad = [Theta1Grad(:) ; Theta2Grad(:)];
end
Normalize the inputs before passing it in nnCostFunction.
In accordance with Week 5 Lecture Notes guideline for a Linear System NN you should make following changes in the initial code:
Remove num_lables or make it 1 (in reshape() as well)
No need to convert y into a logical matrix
For a2 - replace sigmoid() function to tanh()
In d2 calculation - replace sigmoidGradient(z2) with (1-tanh(z2).^2)
Remove sigmoid from output layer (a3 = z3)
Replace cost function in the unregularized portion to linear one: J = (1/(2*m))*sum((a3-y).^2)
Create predictLinear(): use predict() function as a basis, replace sigmoid with tanh() for the first layer hypothesis, remove second sigmoid for the second layer hypothesis, remove the line with max() function, use output of the hidden layer hypothesis as a prediction result
Verify your nnCostFunctionLinear() on the test case from the lecture note