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Commit 10f7bda2 authored by SHREYANSH JAIN's avatar SHREYANSH JAIN
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added 18 marks

parent 284aaad9
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Showing with 2093 additions and 25 deletions
Assignment2/autograder.py 100644 → 100755
View file @ 10f7bda2
......@@ -286,7 +286,7 @@ def check_crossEntropyDelta(task_number):
if __name__ == "__main__":
np.random.seed(42)
np.random.seed(7)
print()
correct_status = False
total_marks = 0
......
Assignment2/main.py
View file @ 10f7bda2
......@@ -11,8 +11,19 @@ def taskXor():
# Eg. nn1.addLayer(FullyConnectedLayer(x,y))
###############################################
# TASK 3a (Marks 7) - YOUR CODE HERE
raise NotImplementedError
# raise NotImplementedError
###############################################
YTrain , YVal, YTest = np.array([int(i[1]==1) for i in YTrain]).reshape((-1,1)), np.array([int(i[1]==1) for i in YVal]).reshape((-1,1)), np.array([int(i[1]==1) for i in YTest]).reshape((-1,1))
lr,batchSize,epochs = 0.2,50,10
nn1 = nn.NeuralNetwork(lr, batchSize, epochs)
# Add layers to neural network corresponding to inputs and outputs of given data
input_layer = XTrain.shape[1]
hidden_layer = 2
output_layer = 1
# activation_fn = 'softmax' # 'relu'
nn1.addLayer(nn.FullyConnectedLayer(input_layer,hidden_layer,'relu'))
nn1.addLayer(nn.FullyConnectedLayer(hidden_layer,output_layer,'relu'))
nn1.train(XTrain, YTrain, XVal, YVal)
pred, acc = nn1.validate(XTest, YTest)
with open("predictionsXor.csv", 'w') as file:
......@@ -24,24 +35,31 @@ def taskXor():
return nn1
def preprocessMnist(X):
# Perform any data preprocessing that you wish to do here
# Input: A 2-d numpy array containing an entire train, val or test split | Shape: n x 28*28
# Output: A 2-d numpy array of the same shape as the input (If the size is changed, you will get downstream errors)
###############################################
# TASK 3c (Marks 0) - YOUR CODE HERE
raise NotImplementedError
###############################################
# Perform any data preprocessing that you wish to do here
# Input: A 2-d numpy array containing an entire train, val or test split | Shape: n x 28*28
# Output: A 2-d numpy array of the same shape as the input (If the size is changed, you will get downstream errors)
###############################################
# TASK 3c (Marks 0) - YOUR CODE HERE
# raise NotImplementedError
###############################################
return X
def taskMnist():
XTrain, YTrain, XVal, YVal, XTest, _ = loadMnist()
# Create a NeuralNetwork object 'nn1' as follows with optimal parameters. For parameter definition, refer to nn.py file.
# nn1 = nn.NeuralNetwork(lr, batchSize, epochs)
# Create a NeuralNetwork object 'nn1' as follows with optimal parameters. For parameter definition, refer to py file.
lr,batchSize,epochs = 0.2,256,50
nn1 = nn.NeuralNetwork(lr, batchSize, epochs)
# Add layers to neural network corresponding to inputs and outputs of given data
# Eg. nn1.addLayer(FullyConnectedLayer(x,y))
input_layer = XTrain.shape[1]
hidden_layer = 256
output_layer = 10
# activation_fn = 'softmax' # 'relu'
nn1.addLayer(nn.FullyConnectedLayer(input_layer,hidden_layer,'relu'))
nn1.addLayer(nn.FullyConnectedLayer(hidden_layer,output_layer,'softmax'))
###############################################
# TASK 3b (Marks 13) - YOUR CODE HERE
raise NotImplementedError
# raise NotImplementedError
###############################################
nn1.train(XTrain, YTrain, XVal, YVal)
pred, _ = nn1.validate(XTest, None)
......@@ -80,11 +98,11 @@ def loadMnist():
testY = None # For MNIST the test labels have not been provided
trainX, trainY = preprocessMnist(np.array(train[0][:50000])), np.array(oneHotEncodeY(train[1][:50000],10))
valX, valY = preprocessMnist(np.array(train[0][50000:])), np.array(oneHotEncodeY(train[1][50000:],10))
return trainX, trainY, valX, valY, testX, testY
#################################################################################################
if __name__ == "__main__":
np.random.seed(7)
taskXor()
taskMnist()
# taskMnist()
Assignment2/nn.py
View file @ 10f7bda2
......@@ -17,19 +17,37 @@ class NeuralNetwork:
# Method to add layers to the Neural Network
self.layers.append(layer)
def train(self, trainX, trainY, validX=None, validY=None):
def train(self, trainX, trainY, validX=None, validY=None):
# Method for training the Neural Network
# Input
# trainX - A list of training input data to the neural network
# trainY - Corresponding list of training data labels
# validX - A list of validation input data to the neural network
# validY - Corresponding list of validation data labels
for i in range(self.epochs):
dataSize = trainX.shape[0]
rndIndex = np.random.permutation(dataSize)
trainX,trainY = trainX[rndIndex],trainY[rndIndex]
print('Epoch',i+1)
batchDataX = trainX[:self.batchSize]
batchDataY = trainY[:self.batchSize]
# prediction = predict(batchDataX)
activations = []
activations.append(batchDataX)
for l in self.layers:
activations.append(l.forwardpass(activations[-1]))
L = self.crossEntropyLoss(batchDataY,activations[-1])
delta = self.crossEntropyDelta(batchDataY,activations[-1])
for l in reversed(self.layers):
prev_activation = activations[self.layers.index(l)]
delta = l.backwardpass(prev_activation,delta)
l.updateWeights(0.01)
# The methods trains the weights and baises using the training data(trainX, trainY)
# Feel free to print accuracy at different points using the validate() or computerAccuracy() functions of this class
###############################################
# TASK 2c (Marks 0) - YOUR CODE HERE
raise NotImplementedError
# raise NotImplementedError
###############################################
def crossEntropyLoss(self, Y, predictions):
......@@ -38,6 +56,7 @@ class NeuralNetwork:
# predictions : Predictions of the model | shape = batchSize x number of output labels
# Returns the cross-entropy loss between the predictions and the ground truth labels | shape = scalar
###############################################
return np.sum((-1)*Y*np.log(predictions))
# TASK 2a (Marks 3) - YOUR CODE HERE
raise NotImplementedError
###############################################
......@@ -50,6 +69,8 @@ class NeuralNetwork:
# output of the last layer of the network | shape = batchSize x number of output labels
###############################################
# TASK 2b (Marks 3) - YOUR CODE HERE
predictions[predictions==0] = 1e-8
return -Y/predictions
raise NotImplementedError
###############################################
......@@ -108,9 +129,9 @@ class FullyConnectedLayer:
# Create np arrays of appropriate sizes for weights and biases and initialise them as you see fit
###############################################
# TASK 1a (Marks 0) - YOUR CODE HERE
raise NotImplementedError
self.weights = None
self.biases = None
# raise NotImplementedError
self.weights = np.random.randn(in_nodes,out_nodes)/np.sqrt(in_nodes)
self.biases = np.zeros((out_nodes,1))
###############################################
# NOTE: You must NOT change the above code but you can add extra variables if necessary
......@@ -124,6 +145,8 @@ class FullyConnectedLayer:
# Returns: Activations after one forward pass through this relu layer | shape: batchSize x self.out_nodes
# This will only be called for layers with activation relu
###############################################
X[X<0] = 0
return X
# TASK 1b (Marks 1) - YOUR CODE HERE
raise NotImplementedError
###############################################
......@@ -136,6 +159,9 @@ class FullyConnectedLayer:
# This will only be called for layers with activation relu amd during backwardpass
###############################################
# TASK 1e (Marks 1) - YOUR CODE HERE
X[X<0] = 0
X[X>0] = 1
return X*delta
raise NotImplementedError
###############################################
......@@ -146,6 +172,8 @@ class FullyConnectedLayer:
# This will only be called for layers with activation softmax
###############################################
# TASK 1c (Marks 3) - YOUR CODE HERE
exps = np.exp(X)
return exps / np.sum(exps)
raise NotImplementedError
###############################################
......@@ -158,9 +186,19 @@ class FullyConnectedLayer:
# Hint: You might need to compute Jacobian first
###############################################
# TASK 1f (Marks 7) - YOUR CODE HERE
raise NotImplementedError
nonZero = delta[delta!=0]
indexList = []
for i in range(len(delta)):
temp = list(delta[i])
indexList.append(temp.index(nonZero[i]))
for i in range(len(indexList)):
temp = X[i][indexList[i]]
X[i] = -temp*X[i]
X[i][indexList[i]] = temp*(1-temp)
return X
# raise NotImplementedError
###############################################
def forwardpass(self, X):
# Input
# activations : Activations from previous layer/input | shape: batchSize x self.in_nodes
......@@ -169,8 +207,12 @@ class FullyConnectedLayer:
###############################################
# TASK 1d (Marks 4) - YOUR CODE HERE
if self.activation == 'relu':
self.data = X @ self.weights + self.biases.T
return self.relu_of_X(self.data)
raise NotImplementedError
elif self.activation == 'softmax':
self.data = X @ self.weights + self.biases.T
return self.softmax_of_X(self.data)
raise NotImplementedError
else:
print("ERROR: Incorrect activation specified: " + self.activation)
......@@ -188,6 +230,7 @@ class FullyConnectedLayer:
# Just compute and store the gradients here - do not make the actual updates
###############################################
# TASK 1g (Marks 6) - YOUR CODE HERE
# print(activation_prev.shape,delta.shape)
if self.activation == 'relu':
inp_delta = self.gradient_relu_of_X(self.data, delta)
elif self.activation == 'softmax':
......@@ -195,6 +238,10 @@ class FullyConnectedLayer:
else:
print("ERROR: Incorrect activation specified: " + self.activation)
exit()
self.weightsGrad = (activation_prev.T @ inp_delta)/delta.shape[0]
self.biasesGrad = np.average(inp_delta,axis=0).reshape((delta.shape[1],-1))
new_delta = inp_delta @ self.weights.T
return new_delta
###############################################
def updateWeights(self, lr):
......@@ -204,6 +251,8 @@ class FullyConnectedLayer:
# This function should actually update the weights using the gradients computed in the backwardpass
###############################################
# TASK 1h (Marks 2) - YOUR CODE HERE
raise NotImplementedError
self.weights = self.weights - lr*(self.weightsGrad)
self.biases = self.biases - lr*(self.biasesGrad)
# raise NotImplementedError
###############################################
\ No newline at end of file
Assignment2/predictionsXor.csv 0 → 100644
View file @ 10f7bda2
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