def run(self, states, Q_target=None):
"""
Runs the DQN for a batch of states.
The DQN takes the state and computes Q-values for all possible actions
that can be taken. That is, if there are two actions, the network takes
as input the state s and computes the vector [Q(s, a_1), Q(s, a_2)]
When Q_target == None, return the matrix of Q-values currently computed
by the network for the input states.
When Q_target is passed, it will contain the Q-values which the network
should be producing for the current states. You must return a nn.Graph
which computes the training loss between your current Q-value
predictions and these target values, using nn.SquareLoss.
Inputs:
states: a (batch_size x 4) numpy array
Q_target: a (batch_size x 2) numpy array, or None
Output:
(if Q_target is not None) A nn.Graph instance, where the last added
node is the loss
(if Q_target is None) A (batch_size x 2) numpy array of Q-value
scores, for the two actions
"""
"*** YOUR CODE HERE ***"
graph = nn.Graph([self.W1, self.W2, self.b1, self.b2])
input_x = nn.Input(graph, states)
xW1mt = nn.MatrixMultiply(graph, input_x, self.W1)
xW1b1Add = nn.MatrixVectorAdd(graph, xW1mt, self.b1)
relu = nn.ReLU(graph, xW1b1Add)
reluMult = nn.MatrixMultiply(graph, relu, self.W2)
total = nn.MatrixVectorAdd(graph, reluMult, self.b2)
# graph = nn.Graph([self.W1])
#Q(s,a) = W1Feature1 + W2feature2 + W3feature3 + W4feature4
# input_x = nn.Input(graph, states)
# W1mult = nn.MatrixMultiply(graph, input_x, self.W1)
#W2mult = nn.MatrixMultiply(graph, input_x, self.W2)
#W3mult = nn.MatrixMultiply(graph, input_x, self.W3)
#W4mult = nn.MatrixMultiply(graph, input_x, self.W4)
#W1W2Add = nn.Add(graph, W1mult, W2mult)
#W3W4Add = nn.Add(graph, W3mult, W4mult)
#total = nn.Add(graph, W1W2Add, W3W4Add)
if Q_target is not None:
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, Q_target)
loss_node = nn.SquareLoss(graph, total, input_y)
return graph
else:
"*** YOUR CODE HERE ***"
return graph.get_output(total)
def run(self, states, Q_target=None):
"""
Runs the DQN for a batch of states.
The DQN takes the state and computes Q-values for all possible actions
that can be taken. That is, if there are two actions, the network takes
as input the state s and computes the vector [Q(s, a_1), Q(s, a_2)]
When Q_target == None, return the matrix of Q-values currently computed
by the network for the input states.
When Q_target is passed, it will contain the Q-values which the network
should be producing for the current states. You must return a nn.Graph
which computes the training loss between your current Q-value
predictions and these target values, using nn.SquareLoss.
Inputs:
states: a (batch_size x 4) numpy array
Q_target: a (batch_size x 2) numpy array, or None
Output:
(if Q_target is not None) A nn.Graph instance, where the last added
node is the loss
(if Q_target is None) A (batch_size x 2) numpy array of Q-value
scores, for the two actions
"""
"*** YOUR CODE HERE ***"
#size of the input vector
i = states.shape[1] * 4
#to test and modify
h = 200
if not self.w1:
self.w1 = nn.Variable(self.state_size, h)
if not self.w2:
self.w2 = nn.Variable(h, self.num_actions)
if not self.b1:
self.b1 = nn.Variable(h)
if not self.b2:
self.b2 = nn.Variable(self.num_actions)
graph = nn.Graph([self.w1, self.w2, self.b1, self.b2])
inputNodeX = nn.Input(graph, states)
mult1 = nn.MatrixMultiply(graph, inputNodeX, self.w1)
add1 = nn.MatrixVectorAdd(graph, mult1, self.b1)
relu = nn.ReLU(graph, add1)
mult2 = nn.MatrixMultiply(graph, relu, self.w2)
add2 = nn.MatrixVectorAdd(graph, mult2, self.b2)
if Q_target is not None:
inputNodeY = nn.Input(graph, Q_target)
lossNode = nn.SquareLoss(graph, add2, inputNodeY)
graph.add(lossNode)
# print("q target is not none")
return graph
else:
# return [get_action(state, .5),
# print("q target is none")
return graph.get_output(add2)
def createNN():
if not self.graph:
for i in range(0, self.depth):
#make weight matrix with every layer X by X size
self.weights.append(nn.Variable(len(x), len(x)))
#make bias matrix with each layer being a vector of X by 1 size
self.bias.append(nn.Variable(len(x), 1))
#create graph with initialized weights and bias variables
self.graph = nn.Graph(self.weights +
self.bias) #weight + bias is variable vector
#create input nodes:
input_x = nn.Input(self.graph, x)
input_y = nn.Input(self.graph, y)
#create first layer:
xm = nn.MatrixMultiply(self.graph, self.weights[0], input_x)
xm_plus_b = nn.MatrixVectorAdd(self.graph, xm, self.bias[0])
#create the remaining layers:
for i in range(1, self.depth):
#add nonlinearity for previous layer:
relu = nn.ReLU(self.graph, xm_plus_b)
#create new hidden layer:
xm = nn.MatrixMultiply(self.graph, self.weights[i], relu)
xm_plus_b = nn.MatrixVectorAdd(self.graph, xm, self.bias[i])
#create loss node:
loss = nn.SquareLoss(self.graph, xm_plus_b, input_y)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct labels are known during training, but not at test time.
When correct labels are available, `y` is a (batch_size x 10) numpy
array. Each row in the array is a one-hot vector encoding the correct
class.
Inputs:
x: a (batch_size x 784) numpy array
y: a (batch_size x 10) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 10) numpy array of scores (aka logits)
"""
graph = nn.Graph([self.m, self.b, self.m2, self.b2])
input_x = nn.Input(graph, x)
#============= LAYER 01 ===============#
xm = nn.MatrixMultiply(graph, input_x, self.m)
xm_plus_b = nn.MatrixVectorAdd(graph, xm, self.b)
#============= LAYER 02 ===============#
relu = nn.ReLU(graph, xm_plus_b)
xm2 = nn.MatrixMultiply(graph, relu, self.m2)
xm_plus_b2 = nn.MatrixVectorAdd(graph, xm2, self.b2)
if y is not None:
input_y = nn.Input(graph, y)
loss = nn.SoftmaxLoss(graph, xm_plus_b2, input_y)
return graph
else:
return graph.get_output(xm_plus_b2)
def run(self, states, Q_target=None):
"""
"""
"*** YOUR CODE HERE ***"
graph = nn.Graph(
[self.m1, self.b1, self.m2, self.b2, self.m3, self.b3])
input_x = nn.Input(graph, states)
xm1 = nn.MatrixMultiply(graph, input_x, self.m1)
xm1_plus_b1 = nn.MatrixVectorAdd(graph, xm1, self.b1)
relu1 = nn.ReLU(graph, xm1_plus_b1)
xm2 = nn.MatrixMultiply(graph, relu1, self.m2)
xm2_plus_b2 = nn.MatrixVectorAdd(graph, xm2, self.b2)
relu2 = nn.ReLU(graph, xm2_plus_b2)
xm3 = nn.MatrixMultiply(graph, relu2, self.m3)
m3x_plus_b3 = nn.MatrixVectorAdd(graph, xm3, self.b3)
if Q_target is not None:
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, Q_target)
loss = nn.SquareLoss(graph, m3x_plus_b3, input_y)
graph.add(loss)
return graph
else:
"*** YOUR CODE HERE ***"
result = graph.get_output(m3x_plus_b3)
return result
def run(self, xs, y=None):
"""
Runs the model for a batch of examples.
Although words have different lengths, our data processing guarantees
that within a single batch, all words will be of the same length (L).
Here `xs` will be a list of length L. Each element of `xs` will be a
(batch_size x self.num_chars) numpy array, where every row in the array
is a one-hot vector encoding of a character. For example, if we have a
batch of 8 three-letter words where the last word is "cat", we will have
xs[1][7,0] == 1. Here the index 0 reflects the fact that the letter "a"
is the inital (0th) letter of our combined alphabet for this task.
The correct labels are known during training, but not at test time.
When correct labels are available, `y` is a (batch_size x 5) numpy
array. Each row in the array is a one-hot vector encoding the correct
class.
Your model should use a Recurrent Neural Network to summarize the list
`xs` into a single node that represents a (batch_size x hidden_size)
array, for your choice of hidden_size. It should then calculate a
(batch_size x 5) numpy array of scores, where higher scores correspond
to greater probability of the word originating from a particular
language. You should use `nn.SoftmaxLoss` as your training loss.
Inputs:
xs: a list with L elements (one per character), where each element
is a (batch_size x self.num_chars) numpy array
y: a (batch_size x 5) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 5) numpy array of scores (aka logits)
Hint: you may use the batch_size variable in your code
"""
batch_size = xs[0].shape[0]
"*** YOUR CODE HERE ***"
h = nn.Variable(batch_size, self.dimensionality)
h.data = np.zeros((batch_size, self.dimensionality))
g = nn.Graph([h, self.w1, self.w2, self.w3, self.b])
for x in xs:
h1 = nn.MatrixMultiply(g, h, self.w1)
x2 = nn.MatrixMultiply(g, nn.Input(g, x), self.w2)
h1_add_x2 = nn.Add(g, h1, x2)
add_b = nn.MatrixVectorAdd(g, h1_add_x2, self.b)
relu = nn.ReLU(g, add_b)
h = relu
result = nn.MatrixMultiply(g, h, self.w3)
if y is not None:
"*** YOUR CODE HERE ***"
nn.SoftmaxLoss(g, result, nn.Input(g, y))
return g
else:
"*** YOUR CODE HERE ***"
return g.get_output(result)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
# graph = nn.Graph([self.W1, self.b1, self.W2, self.b2])
# input_x = nn.Input(graph, x)
# xW1 = nn.MatrixMultiply(graph, input_x, self.W1)
# xW1_plus_b1 = nn.MatrixVectorAdd(graph, xW1, self.b1)
# relu = nn.ReLU(graph, xW1_plus_b1)
# xW2 = nn.MatrixMultiply(graph, relu, self.W2)
# sin = nn.MatrixVectorAdd(graph, xW2, self.b2)
"trying 3 layers"
graph = nn.Graph([self.W1, self.b1, self.W2, self.b2, self.W3, self.b3])
input_x = nn.Input(graph, x)
xW1 = nn.MatrixMultiply(graph, input_x, self.W1)
xW1_plus_b1 = nn.MatrixVectorAdd(graph, xW1, self.b1)
relu = nn.ReLU(graph, xW1_plus_b1)
xW2 = nn.MatrixMultiply(graph, relu, self.W2)
xW2_plus_b2 = nn.MatrixVectorAdd(graph, xW2, self.b2)
relu_second = nn.ReLU(graph, xW2_plus_b2)
xW3 = nn.MatrixMultiply(graph, relu_second, self.W3)
sin = nn.MatrixVectorAdd(graph, xW3, self.b3)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, y)
loss_node = nn.SquareLoss(graph, sin, input_y)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
return graph.get_output(sin)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
#batch_size = x.shape[0]
# set up the graph
oddRegressionGraph = nn.Graph([self.W1, self.b1, self.W2, self.b2])
input_x = nn.Input(oddRegressionGraph, x)
xW1 = nn.MatrixMultiply(oddRegressionGraph, input_x, self.W1)
xW1_plus_b1 = nn.MatrixVectorAdd(oddRegressionGraph, xW1, self.b1)
ReLU_1 = nn.ReLU(oddRegressionGraph, xW1_plus_b1)
R1W2 = nn.MatrixMultiply(oddRegressionGraph, ReLU_1, self.W2)
R1W2_plus_b2 = nn.MatrixVectorAdd(oddRegressionGraph, R1W2, self.b2)
negx = nn.Input(oddRegressionGraph, x * -1)
negxW1 = nn.MatrixMultiply(oddRegressionGraph, negx, self.W1)
negxW1_plus_b1 = nn.MatrixVectorAdd(oddRegressionGraph, negxW1,
self.b1)
ReLU_2 = nn.ReLU(oddRegressionGraph, negxW1_plus_b1)
R2W2 = nn.MatrixMultiply(oddRegressionGraph, ReLU_2, self.W2)
R2W2_plus_b2 = nn.MatrixVectorAdd(oddRegressionGraph, R2W2, self.b2)
#neg2R2W2_plus_b2 = nn.Input(oddRegressionGraph, oddRegressionGraph.get_output(R2W2_plus_b2)*-2)
#negR2W2_plus_b2 = nn.Add(oddRegressionGraph, R2W2_plus_b2, neg2R2W2_plus_b2)
negR2W2_plus_b2 = nn.Input(
oddRegressionGraph,
oddRegressionGraph.get_output(R2W2_plus_b2) * -1)
sumMatrix = nn.Add(oddRegressionGraph, R1W2_plus_b2, negR2W2_plus_b2)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
input_y = nn.Input(oddRegressionGraph, y)
sumMatrix_SL_y = nn.SquareLoss(oddRegressionGraph, sumMatrix,
input_y)
return oddRegressionGraph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
return oddRegressionGraph.get_output(sumMatrix)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct labels are known during training, but not at test time.
When correct labels are available, `y` is a (batch_size x 10) numpy
array. Each row in the array is a one-hot vector encoding the correct
class.
Your model should predict a (batch_size x 10) numpy array of scores,
where higher scores correspond to greater probability of the image
belonging to a particular class. You should use `nn.SoftmaxLoss` as your
training loss.
Inputs:
x: a (batch_size x 784) numpy array
y: a (batch_size x 10) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 10) numpy array of scores (aka logits)
"""
"*** YOUR CODE HERE ***"
self.graph1 = nn.Graph([self.w1,self.b1,self.w2,self.b2,self.w3,self.b3])
self.input_x = nn.Input(self.graph1, x)
if y is not None:
"*** YOUR CODE HERE ***"
self.input_y = nn.Input(self.graph1, y)
xw_1 = nn.MatrixMultiply(self.graph1, self.input_x, self.w1)
xw1_plus_b1 = nn.MatrixVectorAdd(self.graph1, xw_1, self.b1)
relu_l1 = nn.ReLU(self.graph1, xw1_plus_b1)
l1w2 = nn.MatrixMultiply(self.graph1, relu_l1, self.w2)
l1w2_plus_b2 = nn.MatrixVectorAdd(self.graph1, l1w2, self.b2)
relu_l2 = nn.ReLU(self.graph1, l1w2_plus_b2)
l2w3 = nn.MatrixMultiply(self.graph1, relu_l2, self.w3)
l2w3_plus_b3 = nn.MatrixVectorAdd(self.graph1, l2w3, self.b3)
loss = nn.SoftmaxLoss(self.graph1, l2w3_plus_b3, self.input_y)
# print('loss shape',l2w3_plus_b3)
return self.graph1
else:
"*** YOUR CODE HERE ***"
graph2 = nn.Graph([self.w1,self.b1,self.w2,self.b2,self.w3,self.b3])
input_x = nn.Input(graph2, x)
xw_1 = nn.MatrixMultiply(graph2, input_x, self.w1)
xw1_plus_b1 = nn.MatrixVectorAdd(graph2, xw_1, self.b1)
relu_l1 = nn.ReLU(graph2, xw1_plus_b1)
l1w2 = nn.MatrixMultiply(graph2, relu_l1, self.w2)
l1w2_plus_b2 = nn.MatrixVectorAdd(graph2, l1w2, self.b2)
relu_l2 = nn.ReLU(graph2, l1w2_plus_b2)
l2w3 = nn.MatrixMultiply(graph2, relu_l2, self.w3)
l2w3_plus_b3 = nn.MatrixVectorAdd(graph2, l2w3, self.b3)
return graph2.get_output(l2w3_plus_b3)
def run(self, states, Q_target=None):
"""
Runs the DQN for a batch of states.
The DQN takes the state and computes Q-values for all possible actions
that can be taken. That is, if there are two actions, the network takes
as input the state s and computes the vector [Q(s, a_1), Q(s, a_2)]
When Q_target == None, return the matrix of Q-values currently computed
by the network for the input states.
When Q_target is passed, it will contain the Q-values which the network
should be producing for the current states. You must return a nn.Graph
which computes the training loss between your current Q-value
predictions and these target values, using nn.SquareLoss.
Inputs:
states: a (batch_size x 4) numpy array
Q_target: a (batch_size x 2) numpy array, or None
Output:
(if Q_target is not None) A nn.Graph instance, where the last added
node is the loss
(if Q_target is None) A (batch_size x 2) numpy array of Q-value
scores, for the two actions
"""
"*** YOUR CODE HERE ***"
if not self.graph:
w1 = nn.Variable(4, 50)
w2 = nn.Variable(50, 50)
w3 = nn.Variable(50, 2)
b1 = nn.Variable(1, 50)
b2 = nn.Variable(1, 50)
b3 = nn.Variable(1, 2)
self.list = [w1, w2, w3, b1, b2, b3]
self.graph = nn.Graph(self.l)
self.graph = nn.Graph(self.l)
input_x = nn.Input(self.graph, states)
if Q_target is not None:
input_y = nn.Input(self.graph, Q_target)
mult = nn.MatrixMultiply(self.graph, input_x, self.list[0])
add = nn.MatrixVectorAdd(self.graph, mult, self.list[3])
relu = nn.ReLU(self.graph, add)
mult2 = nn.MatrixMultiply(self.graph, relu, self.list[1])
add2 = nn.MatrixVectorAdd(self.graph, mult2, self.list[4])
relu2 = nn.ReLU(self.graph, add2)
mult3 = nn.MatrixMultiply(self.graph, relu2, self.list[2])
add3 = nn.MatrixVectorAdd(self.graph, mult3, self.list[5])
if Q_target is not None:
"* YOUR CODE HERE *"
loss = nn.SquareLoss(self.graph, add3, input_y)
return self.graph
else:
"* YOUR CODE HERE *"
return self.graph.get_output(self.graph.get_nodes()[-1])
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
# positive
graph = nn.Graph([self.w1, self.b1, self.w2, self.b2])
input_x = nn.Input(graph, x)
mul1 = nn.MatrixMultiply(graph, input_x, self.w1)
add1 = nn.MatrixVectorAdd(graph, mul1, self.b1)
hidden_output = nn.ReLU(graph, add1)
mul2 = nn.MatrixMultiply(graph, hidden_output, self.w2)
add2 = nn.MatrixVectorAdd(graph, mul2, self.b2)
# negative
neg_input_x = nn.Input(graph, np.dot(x, np.array([[-1]])))
neg_mul1 = nn.MatrixMultiply(graph, neg_input_x, self.w1)
neg_add1 = nn.MatrixVectorAdd(graph, neg_mul1, self.b1)
neg_hidden_output = nn.ReLU(graph, neg_add1)
neg_mul2 = nn.MatrixMultiply(graph, neg_hidden_output, self.w2)
neg_add2 = nn.MatrixVectorAdd(graph, neg_mul2, self.b2)
neg_mul3 = nn.MatrixMultiply(graph, neg_add2,
nn.Input(graph, np.array([[-1.0]])))
multiLayer = nn.MatrixVectorAdd(graph, add2, neg_mul3)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
inputY = nn.Input(graph, y)
loss = nn.SquareLoss(graph, multiLayer, inputY)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
return graph.get_output(multiLayer)
def run(self, x, y=None):
"""
TODO: Question 6 - [Application] Digit Classification
Runs the model for a batch of examples.
The correct labels are known during training, but not at test time.
When correct labels are available, `y` is a (batch_size x 10) numpy
array. Each row in the array is a one-hot vector encoding the correct
class.
Your model should predict a (batch_size x 10) numpy array of scores,
where higher scores correspond to greater probability of the image
belonging to a particular class. You should use `nn.SoftmaxLoss` as your
training loss.
Inputs:
x: a (batch_size x 784) numpy array
y: a (batch_size x 10) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 10) numpy array of scores (aka logits)
"""
"*** YOUR CODE HERE ***"
#to implement f(x) = relu(x.w1+b1).w2 + b2
graph = nn.Graph([self.w1, self.b1, self.w2, self.b2])
input_x = nn.Input(graph, x)
#input_y = Input(graph, y)
#a = x.w1
a = nn.MatrixMultiply(graph, input_x, self.w1)
#relu(a+b1).w2 + b2
#b = a + b1
b = nn.MatrixVectorAdd(graph, a, self.b1)
#relu(b).w2 + b2
two_layer_relu = nn.ReLU(graph, b)
#c = relu(b).w2
c = nn.MatrixMultiply(graph, two_layer_relu, self.w2)
#d = c + b2
d = nn.MatrixVectorAdd(graph, c, self.b2)
#loss = SquareLoss(graph, xm_plus_b, input_y)
if y is not None:
"*** YOUR CODE HERE ***"
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, y)
loss = nn.SoftmaxLoss(graph, d, input_y)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
return graph.get_output(d)
def run(self, x, y=None):
"""
TODO: Question 5 - [Application] OddRegression
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
graph = nn.Graph([self.w1, self.w2, self.b1, self.b2])
input_x = nn.Input(graph, x)
xm = nn.MatrixMultiply(graph, input_x, self.w1)
xm_plus_b = nn.MatrixVectorAdd(graph, xm, self.b1)
relu = nn.ReLU(graph, xm_plus_b)
w2mul = nn.MatrixMultiply(graph, relu, self.w2)
b2add = nn.MatrixVectorAdd(graph, w2mul, self.b2)
graph2 = nn.Graph([self.w1, self.w2, self.b1, self.b2])
input_x2 = nn.Input(graph2, x * -1)
inputsmall = nn.Input(graph2, np.array([-1.0]))
xm2 = nn.MatrixMultiply(graph2, input_x2, self.w1)
xm_plus_b2 = nn.MatrixVectorAdd(graph2, xm2, self.b1)
relu2 = nn.ReLU(graph2, xm_plus_b2)
w2mul2 = nn.MatrixMultiply(graph2, relu2, self.w2)
b2add2 = nn.MatrixVectorAdd(graph2, w2mul2, self.b2)
#final = nn.MatrixMultiply(graph2, b2add2, inputsmall)
fuckinput = nn.Input(graph,
graph2.get_output(graph2.get_nodes()[-1]) * -1)
realfinal = nn.MatrixVectorAdd(graph, b2add, fuckinput)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, y)
loss = nn.SquareLoss(graph, realfinal, input_y)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
#print graph.get_output(graph.get_nodes()[-1])
#print graph.get_output(input_x)
return graph.get_output(graph.get_nodes()[-1])
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
graph = nn.Graph([self.w1, self.w2, self.b1, self.b2])
input_x = nn.Input(graph, x)
xw1 = nn.MatrixMultiply(graph, input_x, self.w1)
xw1_b1 = nn.MatrixVectorAdd(graph, xw1, self.b1)
relu = nn.ReLU(graph, xw1_b1)
xw2 = nn.MatrixMultiply(graph, relu, self.w2)
xw2_b2 = nn.MatrixVectorAdd(graph, xw2, self.b2)
negate = nn.Input(graph, np.array([[-1.]]))
neg_x = nn.MatrixMultiply(graph, input_x, negate)
neg_xw1 = nn.MatrixMultiply(graph, neg_x, self.w1)
neg_xw1_b1 = nn.MatrixVectorAdd(graph, neg_xw1, self.b1)
neg_relu = nn.ReLU(graph, neg_xw1_b1)
neg_xw2 = nn.MatrixMultiply(graph, neg_relu, self.w2)
neg_xw2_b2 = nn.MatrixVectorAdd(graph, neg_xw2, self.b2)
neg_neg = nn.MatrixMultiply(graph, neg_xw2_b2, negate)
final = nn.MatrixVectorAdd(graph, xw2_b2, neg_neg)
# print graph.get_output(graph.get_nodes()[-1])
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, y)
loss = nn.SquareLoss(graph, final, input_y)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
# # print graph.get_output(graph.get_nodes()[-1])
return graph.get_output(graph.get_nodes()[-1])
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct labels are known during training, but not at test time.
When correct labels are available, `y` is a (batch_size x 10) numpy
array. Each row in the array is a one-hot vector encoding the correct
class.
Your model should predict a (batch_size x 10) numpy array of scores,
where higher scores correspond to greater probability of the image
belonging to a particular class. You should use `nn.SoftmaxLoss` as your
training loss.
Inputs:
x: a (batch_size x 784) numpy array
y: a (batch_size x 10) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 10) numpy array of scores (aka logits)
"""
"*** YOUR CODE HERE ***"
if len(x) == 1:
return 0
if not self.graph:
w1 = nn.Variable(784, 500)
w2 = nn.Variable(500, 500)
w3 = nn.Variable(500, 10)
b1 = nn.Variable(1, 500)
b2 = nn.Variable(1, 500)
b3 = nn.Variable(1, 10)
self.l = [w1, w2, w3, b1, b2, b3]
self.graph = nn.Graph(self.l)
self.graph = nn.Graph(self.l)
input_x = nn.Input(self.graph, x) #Tx784
if y is not None: #<--- THIS LITTLE CONDITIONAL SO IMPORTANT HFS
input_y = nn.Input(self.graph, y)
mult = nn.MatrixMultiply(self.graph, input_x, self.l[0]) #Tx50
add = nn.MatrixVectorAdd(self.graph, mult, self.l[3])
relu = nn.ReLU(self.graph, add)
mult2 = nn.MatrixMultiply(self.graph, relu, self.l[1]) #Tx50
add2 = nn.MatrixVectorAdd(self.graph, mult2, self.l[4]) #Tx50
relu2 = nn.ReLU(self.graph, add2)
mult3 = nn.MatrixMultiply(self.graph, relu2, self.l[2])
add3 = nn.MatrixVectorAdd(self.graph, mult3, self.l[5])
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
loss = nn.SoftmaxLoss(self.graph, add3, input_y)
return self.graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
#print(self.graph.get_output(self.graph.get_nodes()[-1]))
return self.graph.get_output(self.graph.get_nodes()[-1])
def run(self, states, Q_target=None):
"""
TODO: Question 7 - [Application] Reinforcement Learning
Runs the DQN for a batch of states.
The DQN takes the state and computes Q-values for all possible actions
that can be taken. That is, if there are two actions, the network takes
as input the state s and computes the vector [Q(s, a_1), Q(s, a_2)]
When Q_target == None, return the matrix of Q-values currently computed
by the network for the input states.
When Q_target is passed, it will contain the Q-values which the network
should be producing for the current states. You must return a nn.Graph
which computes the training loss between your current Q-value
predictions and these target values, using nn.SquareLoss.
Inputs:
states: a (batch_size x 4) numpy array
Q_target: a (batch_size x 2) numpy array, or None
Output:
(if Q_target is not None) A nn.Graph instance, where the last added
node is the loss
(if Q_target is None) A (batch_size x 2) numpy array of Q-value
scores, for the two actions
"""
"*** YOUR CODE HERE ***"
#to implement f(x) = relu(x.w1+b1).w2 + b2
graph = nn.Graph([self.w1, self.b1, self.w2, self.b2])
input_x = nn.Input(graph, states)
#input_y = Input(graph, y)
#a = x.w1
a = nn.MatrixMultiply(graph, input_x, self.w1)
#relu(a+b1).w2 + b2
#b = a + b1
b = nn.MatrixVectorAdd(graph, a, self.b1)
#relu(b).w2 + b2
two_layer_relu = nn.ReLU(graph, b)
#c = relu(b).w2
c = nn.MatrixMultiply(graph, two_layer_relu, self.w2)
#d = c + b2
d = nn.MatrixVectorAdd(graph, c, self.b2)
#loss = SquareLoss(graph, xm_plus_b, input_y)
if Q_target is not None:
"*** YOUR CODE HERE ***"
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, Q_target)
loss = nn.SquareLoss(graph, d, input_y)
return graph
else:
"*** YOUR CODE HERE ***"
return graph.get_output(d)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
self.graph = nn.Graph([self.m1, self.m2, self.b1, self.b2])
input_x = nn.Input(self.graph, x)
matmult = nn.MatrixMultiply(self.graph, input_x, self.m1)
matadd = nn.MatrixVectorAdd(self.graph, matmult, self.b1)
relu = nn.ReLU(self.graph, matadd)
matmult = nn.MatrixMultiply(self.graph, relu, self.m2)
matadd1 = nn.MatrixVectorAdd(self.graph, matmult, self.b2)
matrixNeg = nn.Input(self.graph, np.array([[-1.]]))
matmultNeg = nn.MatrixMultiply(self.graph, input_x, matrixNeg)
matmult = nn.MatrixMultiply(self.graph, matmultNeg, self.m1)
matadd = nn.MatrixVectorAdd(self.graph, matmult, self.b1)
relu = nn.ReLU(self.graph, matadd)
matmult = nn.MatrixMultiply(self.graph, relu, self.m2)
matadd2 = nn.MatrixVectorAdd(self.graph, matmult, self.b2)
negMat = nn.MatrixMultiply(self.graph, matadd2, matrixNeg)
func = nn.MatrixVectorAdd(self.graph, matadd1, negMat)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
input_y = nn.Input(self.graph, y)
loss = nn.SquareLoss(self.graph, func, input_y)
# self.graph.add(loss)
return self.graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
return self.graph.get_output(func)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
graph = nn.Graph([self.w_one, self.w_two, self.b_one, self.b_two])
input_i = nn.Input(graph, x)
mul = nn.MatrixMultiply(graph, input_i, self.w_one)
add = nn.MatrixVectorAdd(graph, mul, self.b_one)
relu = nn.ReLU(graph, add)
# -x
tempx = np.negative(x)
input_i2 = nn.Input(graph, tempx)
multiply = nn.MatrixMultiply(graph, input_i2, self.w_one)
addition = nn.MatrixVectorAdd(graph, multiply, self.b_one)
relu2 = nn.ReLU(graph, addition)
# -f(-x)
arr = np.negative(np.identity(self.hidden_size))
negativeOne = nn.Input(graph, arr)
negatedRelu = nn.MatrixMultiply(graph, relu2, negativeOne)
# f(x) + (-f(-x))
inside = nn.MatrixVectorAdd(graph, relu, negatedRelu)
mul2 = nn.MatrixMultiply(graph, inside, self.w_two)
#add2 = nn.MatrixVectorAdd(graph, mul2, self.b_two)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
y_in = nn.Input(graph, y)
loss = nn.SquareLoss(graph, mul2, y_in)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
return graph.get_output(mul2)
def run(self, xs, y=None):
"""
Runs the model for a batch of examples.
Although words have different lengths, our data processing guarantees
that within a single batch, all words will be of the same length (L).
Here `xs` will be a list of length L. Each element of `xs` will be a
(batch_size x self.num_chars) numpy array, where every row in the array
is a one-hot vector encoding of a character. For example, if we have a
batch of 8 three-letter words where the last word is "cat", we will have
xs[1][7,0] == 1. Here the index 0 reflects the fact that the letter "a"
is the inital (0th) letter of our combined alphabet for this task.
The correct labels are known during training, but not at test time.
When correct labels are available, `y` is a (batch_size x 5) numpy
array. Each row in the array is a one-hot vector encoding the correct
class.
Your model should use a Recurrent Neural Network to summarize the list
`xs` into a single node that represents a (batch_size x hidden_size)
array, for your choice of hidden_size. It should then calculate a
(batch_size x 5) numpy array of scores, where higher scores correspond
to greater probability of the word originating from a particular
language. You should use `nn.SoftmaxLoss` as your training loss.
Inputs:
xs: a list with L elements (one per character), where each element
is a (batch_size x self.num_chars) numpy array
y: a (batch_size x 5) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 5) numpy array of scores (aka logits)
Hint: you may use the batch_size variable in your code
"""
batch_size = xs[0].shape[0]
graph = nn.Graph([self.C_training, self.H_traing, self.m, self.b])
H = np.zeros((batch_size, self.hidden_size))
inputH = nn.Input(graph, H)
for X in xs:
inputX = nn.Input(graph, X)
CWx = nn.MatrixMultiply(graph, inputX, self.C_training)
HWh = nn.MatrixMultiply(graph, inputH, self.H_traing)
inputH = nn.ReLU(graph, nn.Add(graph, CWx, HWh))
xm = nn.MatrixMultiply(graph, inputH, self.m)
xm_plus_b = nn.MatrixVectorAdd(graph, xm, self.b)
if y is not None:
input_y = nn.Input(graph, y)
nn.SquareLoss(graph, xm_plus_b, input_y)
return graph
else:
return graph.get_output(xm_plus_b)
def run(self, x, y=None):
"""
TODO: Question 5 - [Application] OddRegression
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
# calculates g(x)
graph = nn.Graph([self.w_one, self.b_one, self.w_two, self.b_two])
input_x = nn.Input(graph, x)
xw = nn.MatrixMultiply(graph, input_x, self.w_one)
xw_plus_b = nn.MatrixVectorAdd(graph, xw, self.b_one)
relu = nn.ReLU(graph, xw_plus_b)
reluw = nn.MatrixMultiply(graph, relu, self.w_two)
reluw_plus_b = nn.MatrixVectorAdd(graph, reluw, self.b_two)
# calculates g(-x)
negone = nn.Input(graph, np.array([[-1.0]]))
neg_x = nn.MatrixMultiply(graph, input_x, negone)
negxw = nn.MatrixMultiply(graph, neg_x, self.w_one)
negxw_plus_b = nn.MatrixVectorAdd(graph, negxw, self.b_one)
negrelu = nn.ReLU(graph, negxw_plus_b)
negreluw = nn.MatrixMultiply(graph, negrelu, self.w_two)
negreluw_plus_b = nn.MatrixVectorAdd(graph, negreluw, self.b_two)
#g(x)-(g(-x))
negG = nn.MatrixMultiply(graph, negreluw_plus_b, negone)
oddFunc = nn.Add(graph, reluw_plus_b, negG)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, y)
loss = nn.SquareLoss(graph, oddFunc, input_y)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
return graph.get_output(oddFunc)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
graph = nn.Graph([self.w1,self.b1,self.w2])
xInput = nn.Input(graph,x)
negXInput = nn.Input(graph,-x)
yInput = nn.Input(graph,y)
layer1 = nn.MatrixMultiply(graph, xInput, self.w1)
negLayer1 = nn.MatrixMultiply(graph, negXInput, self.w1)
layer2 = nn.MatrixVectorAdd(graph, layer1, self.b1)
negLayer2 = nn.MatrixVectorAdd(graph, negLayer1, self.b1)
layer3 = nn.ReLU(graph, layer2)
negLayer3 = nn.ReLU(graph, negLayer2)
combinedLayer = nn.Subtract(graph, layer3, negLayer3)
layer4 = nn.MatrixMultiply(graph, combinedLayer, self.w2)
layer5 = nn.SquareLoss(graph, layer4, yInput)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
graph = nn.Graph([self.w1,self.b1,self.w2])
xInput = nn.Input(graph,x)
negXInput = nn.Input(graph,-x)
layer1 = nn.MatrixMultiply(graph, xInput, self.w1)
negLayer1 = nn.MatrixMultiply(graph, negXInput, self.w1)
layer2 = nn.MatrixVectorAdd(graph, layer1, self.b1)
negLayer2 = nn.MatrixVectorAdd(graph, negLayer1, self.b1)
layer3 = nn.ReLU(graph, layer2)
negLayer3 = nn.ReLU(graph, negLayer2)
combinedLayer = nn.Subtract(graph, layer3, negLayer3)
layer4 = nn.MatrixMultiply(graph, combinedLayer, self.w2)
return graph.get_output(layer4)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct labels are known during training, but not at test time.
When correct labels are available, y is a (batch_size x 10) numpy
array. Each row in the array is a one-hot vector encoding the correct
class.
Your model should predict a (batch_size x 10) numpy array of scores,
where higher scores correspond to greater probability of the image
belonging to a particular class. You should use nn.SoftmaxLoss as your
training loss.
Inputs:
x: a (batch_size x 784) numpy array
y: a (batch_size x 10) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 10) numpy array of scores (aka logits)
"""
"* YOUR CODE HERE *"
graph = nn.Graph([
self.weight1, self.bias1, self.weight2, self.bias2, self.weight3,
self.bias3, self.weight4, self.bias4, self.weight5, self.bias5
])
input_x = nn.Input(graph, x)
xw1 = nn.MatrixMultiply(graph, input_x, self.weight1)
plus1b1 = nn.MatrixVectorAdd(graph, xw1, self.bias1)
relu1 = nn.ReLU(graph, plus1b1)
relu1_2 = nn.MatrixMultiply(graph, relu1, self.weight2)
plus2b2 = nn.MatrixVectorAdd(graph, relu1_2, self.bias2)
relu2 = nn.ReLU(graph, plus2b2)
relu2_3 = nn.MatrixMultiply(graph, relu2, self.weight3)
plus3b3 = nn.MatrixVectorAdd(graph, relu2_3, self.bias3)
relu3 = nn.ReLU(graph, plus3b3)
relu3_4 = nn.MatrixMultiply(graph, relu3, self.weight4)
plus4b4 = nn.MatrixVectorAdd(graph, relu3_4, self.bias4)
relu4 = nn.ReLU(graph, plus4b4)
relu4_5 = nn.MatrixMultiply(graph, relu4, self.weight5)
plus5b5 = nn.MatrixVectorAdd(graph, relu4_5, self.bias5)
if y is not None:
"* YOUR CODE HERE *"
input_y = nn.Input(graph, y)
loss = nn.SoftmaxLoss(graph, plus5b5, input_y)
return graph
else:
"* YOUR CODE HERE *"
return graph.get_output(plus5b5)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
graph = nn.Graph([self.w1, self.b1, self.w2, self.b2])
input_x = nn.Input(graph, x)
nn_mm_w1 = nn.MatrixMultiply(graph, input_x, self.w1)
nn_mm_b1 = nn.MatrixVectorAdd(graph, nn_mm_w1, self.b1)
nn_rl_b1 = nn.ReLU(graph, nn_mm_b1)
nn_mm_w2 = nn.MatrixMultiply(graph, nn_rl_b1, self.w2)
nn_mm_b2 = nn.MatrixVectorAdd(graph, nn_mm_w2, self.b2)
mx = np.dot(-1, x)
nn_mx = nn.Input(graph, mx)
mm_mx = nn.MatrixMultiply(graph, nn_mx, self.w1)
mm_mx_b1 = nn.MatrixVectorAdd(graph, mm_mx, self.b1)
rl_mx_b1 = nn.ReLU(graph, mm_mx_b1)
mm_mx_w2 = nn.MatrixMultiply(graph, rl_mx_b1, self.w2)
mm_mx_b2 = nn.MatrixVectorAdd(graph, mm_mx_w2, self.b2)
new_x = nn.Subtract(graph, nn_mm_b2, mm_mx_b2)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
nn_y = nn.Input(graph, y)
loss = nn.SquareLoss(graph, new_x, nn_y)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
return graph.get_output(new_x)
def run(self, states, Q_target=None):
"""
TODO: Question 7 - [Application] Reinforcement Learning
Runs the DQN for a batch of states.
The DQN takes the state and computes Q-values for all possible actions
that can be taken. That is, if there are two actions, the network takes
as input the state s and computes the vector [Q(s, a_1), Q(s, a_2)]
When Q_target == None, return the matrix of Q-values currently computed
by the network for the input states.
When Q_target is passed, it will contain the Q-values which the network
should be producing for the current states. You must return a nn.Graph
which computes the training loss between your current Q-value
predictions and these target values, using nn.SquareLoss.
Inputs:
states: a (batch_size x 4) numpy array
Q_target: a (batch_size x 2) numpy array, or None
Output:
(if Q_target is not None) A nn.Graph instance, where the last added
node is the loss
(if Q_target is None) A (batch_size x 2) numpy array of Q-value
scores, for the two actions
"""
"*** YOUR CODE HERE ***"
hidden_layer_size = 150
if not self.w1:
self.w1 = nn.Variable(self.state_size, hidden_layer_size)
if not self.w2:
self.w2 = nn.Variable(hidden_layer_size, self.num_actions)
if not self.b1:
self.b1 = nn.Variable(hidden_layer_size)
if not self.b2:
self.b2 = nn.Variable(self.num_actions)
g = nn.Graph([self.w1, self.w2, self.b1, self.b2])
result = nn.MatrixVectorAdd(
g,
nn.MatrixMultiply(
g,
nn.ReLU(
g,
nn.MatrixVectorAdd(
g, nn.MatrixMultiply(g, nn.Input(g, states), self.w1),
self.b1)), self.w2), self.b2)
if Q_target is not None:
"*** YOUR CODE HERE ***"
g.add(nn.SquareLoss(g, result, nn.Input(g, Q_target)))
return g
else:
"*** YOUR CODE HERE ***"
return g.get_output(result)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
graph = nn.Graph(self.variables)
negative1 = -1*np.ones((1,1))
input_x = nn.Input(graph, x)
neg_1 = nn.Input(graph, negative1)
"""First we do the positives"""
xw1 = nn.MatrixMultiply(graph, input_x, self.variables[0])
sumxw1b1 = nn.MatrixVectorAdd(graph, xw1, self.variables[1])
relu = nn.ReLU(graph, sumxw1b1)
reluW2 = nn.MatrixMultiply(graph, relu, self.variables[2])
"""Now we do the negatives"""
negx = nn.MatrixMultiply(graph, input_x, neg_1)
nxw1 = nn.MatrixMultiply(graph, negx, self.variables[0])
sumnxw1 = nn.MatrixVectorAdd(graph, nxw1, self.variables[1])
nrelu = nn.ReLU(graph, sumnxw1)
nreluW2 = nn.MatrixMultiply(graph, nrelu, self.variables[2])
"""Set the negative value of negative x to negative"""
nsumNRW2b2 = nn.MatrixMultiply(graph, nreluW2, neg_1)
"""Add the two sums together"""
totalSum = nn.Add(graph, reluW2, nsumNRW2b2)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, y)
loss = nn.SquareLoss(graph, totalSum, input_y)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
nodes = graph.get_nodes()
lastnode = nodes[-1]
out = graph.get_output(lastnode)
return out
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct labels are known during training, but not at test time.
When correct labels are available, `y` is a (batch_size x 10) numpy
array. Each row in the array is a one-hot vector encoding the correct
class.
Your model should predict a (batch_size x 10) numpy array of scores,
where higher scores correspond to greater probability of the image
belonging to a particular class. You should use `nn.SoftmaxLoss` as your
training loss.
Inputs:
x: a (batch_size x 784) numpy array
y: a (batch_size x 10) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 10) numpy array of scores (aka logits)
"""
"*** YOUR CODE HERE ***"
graph = nn.Graph([
self.m0, self.b0, self.m1, self.b1, self.m2, self.b2, self.m3,
self.b3, self.m4, self.b4, self.m5, self.b5
])
input_x = nn.Input(graph, x)
t = nn.MatrixMultiply(graph, input_x, self.m0)
t = nn.MatrixVectorAdd(graph, t, self.b0)
t = nn.ReLU(graph, t)
t = nn.MatrixMultiply(graph, t, self.m1)
t = nn.MatrixVectorAdd(graph, t, self.b1)
t = nn.ReLU(graph, t)
t = nn.MatrixMultiply(graph, t, self.m2)
t = nn.MatrixVectorAdd(graph, t, self.b2)
t = nn.ReLU(graph, t)
t = nn.MatrixMultiply(graph, t, self.m3)
t = nn.MatrixVectorAdd(graph, t, self.b3)
t = nn.ReLU(graph, t)
t = nn.MatrixMultiply(graph, t, self.m4)
t = nn.MatrixVectorAdd(graph, t, self.b4)
t = nn.ReLU(graph, t)
t = nn.MatrixMultiply(graph, t, self.m5)
t = nn.MatrixVectorAdd(graph, t, self.b5)
if y is not None:
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, y)
loss = nn.SoftmaxLoss(graph, t, input_y)
return graph
else:
"*** YOUR CODE HERE ***"
res = graph.outputs[graph.get_nodes()[-1]]
return res
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
sign = x < 0
x = np.where(sign, -x, x)
graph = nn.Graph(
[self.m0, self.b0, self.m1, self.b1, self.m2, self.b2])
input_x = nn.Input(graph, x)
t = nn.MatrixMultiply(graph, input_x, self.m0)
t = nn.MatrixVectorAdd(graph, t, self.b0)
t = nn.ReLU(graph, t)
t = nn.MatrixMultiply(graph, t, self.m1)
t = nn.MatrixVectorAdd(graph, t, self.b1)
t = nn.ReLU(graph, t)
t = nn.MatrixMultiply(graph, t, self.m2)
t = nn.MatrixVectorAdd(graph, t, self.b2)
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
y = np.where(sign, -y, y)
input_y = nn.Input(graph, y)
loss = nn.SquareLoss(graph, t, input_y)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
res = graph.outputs[graph.get_nodes()[-1]]
res = np.where(sign, -res, res)
res = np.where(x == 0, np.zeros_like(res), res)
return res
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct labels are known during training, but not at test time.
When correct labels are available, `y` is a (batch_size x 10) numpy
array. Each row in the array is a one-hot vector encoding the correct
class.
Your model should predict a (batch_size x 10) numpy array of scores,
where higher scores correspond to greater probability of the image
belonging to a particular class. You should use `nn.SoftmaxLoss` as your
training loss.
Inputs:
x: a (batch_size x 784) numpy array
y: a (batch_size x 10) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 10) numpy array of scores (aka logits)
"""
"*** YOUR CODE HERE ***"
#print("x", x.shape)
#print("y", y.shape)
graph = nn.Graph(
[self.W1, self.W2, self.W3, self.W4, self.W5, self.W6])
input_x = nn.Input(graph, x)
#first term
xW1mult = nn.MatrixMultiply(graph, input_x, self.W1)
#second term
xW2mult = nn.MatrixMultiply(graph, input_x, self.W2)
addW1W2 = nn.Add(graph, xW1mult, xW2mult)
relu1 = nn.ReLU(graph, addW1W2)
reluMult = nn.MatrixMultiply(graph, relu1, self.W3)
xW4mult = nn.MatrixMultiply(graph, input_x, self.W4)
W4W5mult = nn.MatrixMultiply(graph, xW4mult, self.W5)
per2Add = nn.Add(graph, reluMult, W4W5mult)
totalMult = nn.MatrixMultiply(graph, per2Add, self.W6)
#another term
#lastRelu = nn.ReLU(graph, totalMult)
if y is not None:
"*** YOUR CODE HERE ***"
input_y = nn.Input(graph, y)
loss_node = nn.SoftmaxLoss(graph, totalMult, input_y)
return graph
else:
"*** YOUR CODE HERE ***"
return graph.get_output(totalMult)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
"*** YOUR CODE HERE ***"
graph = nn.Graph(self.param_w + self.param_b)
inX = [nn.Input(graph, x), nn.Input(graph, -1 * x)]
last = inX
for i in range(self.num_layers):
newLast = last
for j in range(len(last)):
multNode = nn.MatrixMultiply(graph, last[j], self.param_w[i])
addNode = nn.MatrixVectorAdd(graph, multNode, self.param_b[i])
if i != self.num_layers - 1:
reluNode = nn.ReLU(graph, addNode)
newLast[j] = reluNode
else:
newLast[j] = addNode
last = newLast
# f(x) = g(x) + -1 * g(-x)
neg = nn.Input(graph, np.array(-1.0))
multNode = nn.MatrixMultiply(graph, inX[1], neg)
addNode = nn.MatrixVectorAdd(graph, inX[0], multNode)
final = addNode
if y is not None:
# At training time, the correct output `y` is known.
# Here, you should construct a loss node, and return the nn.Graph
# that the node belongs to. The loss node must be the last node
# added to the graph.
"*** YOUR CODE HERE ***"
inY = nn.Input(graph, y)
loss = nn.SquareLoss(graph, final, inY)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
"*** YOUR CODE HERE ***"
return graph.get_output(final)
def run(self, x, y=None):
"""
Runs the model for a batch of examples.
The correct outputs `y` are known during training, but not at test time.
If correct outputs `y` are provided, this method must construct and
return a nn.Graph for computing the training loss. If `y` is None, this
method must instead return predicted y-values.
Inputs:
x: a (batch_size x 1) numpy array
y: a (batch_size x 1) numpy array, or None
Output:
(if y is not None) A nn.Graph instance, where the last added node is
the loss
(if y is None) A (batch_size x 1) numpy array of predicted y-values
Note: DO NOT call backprop() or step() inside this method!
"""
graph = nn.Graph([self.w1, self.w2, self.b1, self.b2])
#pos
a_graph = nn.Graph([self.w1, self.w2, self.b1, self.b2])
a_input_x = nn.Input(graph, x)
a_mult1 = nn.MatrixMultiply(graph, a_input_x, self.w1)
a_add1 = nn.MatrixVectorAdd(graph, a_mult1, self.b1)
a_relu1 = nn.ReLU(graph, a_add1)
a_mult2 = nn.MatrixMultiply(graph, a_relu1, self.w2)
a_add2 = nn.MatrixVectorAdd(graph, a_mult2, self.b2)
#neg
b_input_x = nn.Input(graph, np.dot(-1, x))
b_mult1 = nn.MatrixMultiply(graph, b_input_x, self.w1)
b_add1 = nn.MatrixVectorAdd(graph, b_mult1, self.b1)
b_relu1 = nn.ReLU(graph, b_add1)
b_mult2 = nn.MatrixMultiply(graph, b_relu1, self.w2)
b_add2 = nn.MatrixVectorAdd(graph, b_mult2, self.b2)
b_output = graph.get_output(b_add2)
neg_b_output = -1 * b_output
neg_matrix = np.zeros(np.shape(b_output)[1])
neg = np.negative(np.identity(np.shape(b_output)[1]))
neg_b_add2 = nn.Input(graph, neg)
b_mult3 = nn.MatrixMultiply(graph, b_add2, neg_b_add2)
result = nn.Add(graph, a_add2, b_mult3)
if y is not None:
input_y = nn.Input(graph, y)
loss = nn.SquareLoss(graph, result, input_y)
return graph
else:
# At test time, the correct output is unknown.
# You should instead return your model's prediction as a numpy array
return graph.get_output(result)
