def loss(self, X, y=None):
"""
Compute loss and gradient for a minibatch of data.
Inputs:
- X: Tensor of input data of shape (N, d_1, ..., d_k)
- y: int64 Tensor of labels, of shape (N,). y[i] gives the label for X[i].
Returns:
If y is None, then run a test-time forward pass of the model and return:
- scores: Tensor of shape (N, C) giving classification scores, where
scores[i, c] is the classification score for X[i] and class c.
If y is not None, then run a training-time forward and backward pass and
return a tuple of:
- loss: Scalar value giving the loss
- grads: Dictionary with the same keys as self.params, mapping parameter
names to gradients of the loss with respect to those parameters.
"""
scores = None
###########################################################################
# TODO: Implement the forward pass for the two-layer net, computing the #
# class scores for X and storing them in the scores variable. #
###########################################################################
# Replace "pass" statement with your code
h1, cache1 = Linear_ReLU.forward(X, self.params['W1'], self.params['b1'])
scores, cache2 = Linear.forward(h1, self.params['W2'], self.params['b2'])
###########################################################################
# END OF YOUR CODE #
###########################################################################
# If y is None then we are in test mode so just return scores
if y is None:
return scores
loss, grads = 0, {}
###########################################################################
# TODO: Implement the backward pass for the two-layer net. Store the loss #
# in the loss variable and gradients in the grads dictionary. Compute data#
# loss using softmax, and make sure that grads[k] holds the gradients for #
# self.params[k]. Don't forget to add L2 regularization! #
# #
# NOTE: To ensure that your implementation matches ours and you pass the #
# automated tests, make sure that your L2 regularization does not include #
# a factor of 0.5. #
###########################################################################
# Replace "pass" statement with your code
loss, dout = softmax_loss(scores, y)
loss += self.reg * torch.sum(self.params['W1'] ** 2) + self.reg * torch.sum(self.params['W2'] ** 2)
dh1, grads['W2'], grads['b2'] = Linear.backward(dout, cache2)
grads['W2'] += 2 * self.reg * self.params['W2']
_, grads['W1'], grads['b1'] = Linear_ReLU.backward(dh1, cache1)
grads['W1'] += 2 * self.reg * self.params['W1']
###########################################################################
# END OF YOUR CODE #
###########################################################################
return loss, grads
def loss(self, X, y=None):
"""
Compute loss and gradient for the fully-connected net.
Input / output: Same as TwoLayerNet above.
"""
X = X.to(self.dtype)
mode = 'test' if y is None else 'train'
# Set train/test mode for batchnorm params and dropout param since they
# behave differently during training and testing.
if self.use_dropout:
self.dropout_param['mode'] = mode
scores = None
############################################################################
# TODO: Implement the forward pass for the fully-connected net, computing #
# the class scores for X and storing them in the scores variable. #
# #
# When using dropout, you'll need to pass self.dropout_param to each #
# dropout forward pass. #
############################################################################
# Replace "pass" statement with your code
hiddens = {}
caches = {}
dropouts = {}
N = self.num_layers - 1
if self.use_dropout:
dropouts['d0'] = X
for i in range(N):
hiddens[f'h{i+1}'], caches[f'l{i}'] = Linear_ReLU.forward(dropouts[f'd{i}'], self.params[f'W{i}'], self.params[f'b{i}'])
dropouts[f'd{i+1}'], caches[f'd{i+1}'] = Dropout.forward(hiddens[f'h{i+1}'], self.dropout_param)
scores, caches[f'l{N}'] = Linear.forward(dropouts[f'd{N}'], self.params[f'W{N}'], self.params[f'b{N}'])
else:
hiddens['h0'] = X
for i in range(N):
hiddens[f'h{i+1}'], caches[f'l{i}'] = Linear_ReLU.forward(hiddens[f'h{i}'], self.params[f'W{i}'], self.params[f'b{i}'])
scores, caches[f'l{N}'] = Linear.forward(hiddens[f'h{N}'], self.params[f'W{N}'], self.params[f'b{N}'])
############################################################################
# END OF YOUR CODE #
############################################################################
# If test mode return early
if mode == 'test':
return scores
loss, grads = 0.0, {}
############################################################################
# TODO: Implement the backward pass for the fully-connected net. Store the #
# loss in the loss variable and gradients in the grads dictionary. Compute #
# data loss using softmax, and make sure that grads[k] holds the gradients #
# for self.params[k]. Don't forget to add L2 regularization! #
# NOTE: To ensure that your implementation matches ours and you pass the #
# automated tests, make sure that your L2 regularization includes a factor #
# of 0.5 to simplify the expression for the gradient. #
############################################################################
# Replace "pass" statement with your code
# calculate loss
loss, da = softmax_loss(scores, y)
reg_sum = 0.0
for i in range(self.num_layers):
reg_sum += torch.sum(self.params[f'W{i}'] * self.params[f'W{i}'])
loss += self.reg * reg_sum
# calculate gradients
ds = {}
if self.use_dropout:
for i in reversed(range(self.num_layers)):
if i == self.num_layers-1:
ds[f'dh{i}'], ds[f'dW{i}'], ds[f'db{i}'] = Linear.backward(da, caches[f'l{i}'])
else:
ds[f'dd{i+1}'] = Dropout.backward(ds[f'dh{i+1}'], caches[f'd{i+1}'])
ds[f'dh{i}'], ds[f'dW{i}'], ds[f'db{i}'] = Linear_ReLU.backward(ds[f'dd{i+1}'], caches[f'l{i}'])
else:
for i in reversed(range(self.num_layers)):
if i == self.num_layers-1:
ds[f'dh{i}'], ds[f'dW{i}'], ds[f'db{i}'] = Linear.backward(da, caches[f'l{i}'])
else:
ds[f'dh{i}'], ds[f'dW{i}'], ds[f'db{i}'] = Linear_ReLU.backward(ds[f'dh{i+1}'], caches[f'l{i}'])
for i in range(self.num_layers):
grads[f'W{i}'] = ds[f'dW{i}'] + 2 * self.reg * self.params[f'W{i}']
grads[f'b{i}'] = ds[f'db{i}']
############################################################################
# END OF YOUR CODE #
############################################################################
return loss, grads
def loss(self, X, y=None):
"""
Compute loss and gradient for the fully-connected net.
Input / output: Same as TwoLayerNet above.
"""
X = X.to(self.dtype)
mode = 'test' if y is None else 'train'
# Set train/test mode for batchnorm params and dropout param since they
# behave differently during training and testing.
if self.use_dropout:
self.dropout_param['mode'] = mode
scores = None
############################################################################
# TODO: Implement the forward pass for the fully-connected net, computing #
# the class scores for X and storing them in the scores variable. #
# #
# When using dropout, you'll need to pass self.dropout_param to each #
# dropout forward pass. #
############################################################################
# Replace "pass" statement with your code
h = X
relu_caches = {}
dropout_caches = {}
cache = None
for i in range(1, self.num_layers):
h, relu_caches[i] = Linear_ReLU.forward(h, self.params[f'W{i}'], self.params[f'b{i}'])
if self.use_dropout:
h, dropout_caches[i] = Dropout.forward(h, self.dropout_param)
scores, cache = Linear.forward(h, self.params[f'W{self.num_layers}'], self.params[f'b{self.num_layers}'])
############################################################################
# END OF YOUR CODE #
############################################################################
# If test mode return early
if mode == 'test':
return scores
loss, grads = 0.0, {}
############################################################################
# TODO: Implement the backward pass for the fully-connected net. Store the #
# loss in the loss variable and gradients in the grads dictionary. Compute #
# data loss using softmax, and make sure that grads[k] holds the gradients #
# for self.params[k]. Don't forget to add L2 regularization! #
# NOTE: To ensure that your implementation matches ours and you pass the #
# automated tests, make sure that your L2 regularization includes a factor #
# of 0.5 to simplify the expression for the gradient. #
############################################################################
# Replace "pass" statement with your code
loss, dout = softmax_loss(scores, y)
for i in range(1, self.num_layers + 1):
loss += self.reg * torch.sum(self.params[f'W{i}'] ** 2)
dh, grads[f'W{self.num_layers}'], grads[f'b{self.num_layers}'] = Linear.backward(dout, cache)
grads[f'W{self.num_layers}'] += 2 * self.reg * self.params[f'W{self.num_layers}']
for i in range(self.num_layers - 1, 0, -1):
if self.use_dropout:
dh = Dropout.backward(dh, dropout_caches[i])
dh, grads[f'W{i}'], grads[f'b{i}'] = Linear_ReLU.backward(dh, relu_caches[i])
grads[f'W{i}'] += 2 * self.reg * self.params[f'W{i}']
############################################################################
# END OF YOUR CODE #
############################################################################
return loss, grads
def loss(self, X, y=None):
"""
Compute loss and gradient for the fully-connected net.
Input / output: Same as TwoLayerNet above.
"""
X = X.to(self.dtype)
mode = 'test' if y is None else 'train'
# Set train/test mode for batchnorm params and dropout param since they
# behave differently during training and testing.
if self.use_dropout:
self.dropout_param['mode'] = mode
scores = None
############################################################################
# TODO: Implement the forward pass for the fully-connected net, computing #
# the class scores for X and storing them in the scores variable. #
# #
# When using dropout, you'll need to pass self.dropout_param to each #
# dropout forward pass. #
############################################################################
# Replace "pass" statement with your code
cache_dict = {}
last_out = X
for n in range(self.num_layers - 1):
i = n + 1
last_out, cache_dict['cache_LR{}'.format(i)] = Linear_ReLU.forward(
last_out, self.params['W{}'.format(i)],
self.params['b{}'.format(i)])
if self.use_dropout:
last_out, cache_dict['cache_Dropout{}'.format(
i)] = Dropout.forward(last_out, self.dropout_param)
i += 1
last_out, cache_dict['cache_L{}'.format(i)] = Linear.forward(
last_out, self.params['W{}'.format(i)],
self.params['b{}'.format(i)])
scores = last_out
############################################################################
# END OF YOUR CODE #
############################################################################
# If test mode return early
if mode == 'test':
return scores
loss, grads = 0.0, {}
############################################################################
# TODO: Implement the backward pass for the fully-connected net. Store the #
# loss in the loss variable and gradients in the grads dictionary. Compute #
# data loss using softmax, and make sure that grads[k] holds the gradients #
# for self.params[k]. Don't forget to add L2 regularization! #
# NOTE: To ensure that your implementation matches ours and you pass the #
# automated tests, make sure that your L2 regularization includes a factor #
# of 0.5 to simplify the expression for the gradient. #
############################################################################
# Replace "pass" statement with your code
loss, dout = softmax_loss(scores, y)
loss += (self.params['W{}'.format(i)] *
self.params['W{}'.format(i)]).sum() * self.reg
last_dout, dw, db = Linear.backward(dout,
cache_dict['cache_L{}'.format(i)])
grads['W{}'.format(
i)] = dw + 2 * self.params['W{}'.format(i)] * self.reg
grads['b{}'.format(i)] = db
for n in range(self.num_layers - 1)[::-1]:
i = n + 1
if self.use_dropout:
last_dout = Dropout.backward(
last_dout, cache_dict['cache_Dropout{}'.format(i)])
last_dout, dw, db = Linear_ReLU.backward(
last_dout, cache_dict['cache_LR{}'.format(i)])
grads['W{}'.format(
i)] = dw + 2 * self.params['W{}'.format(i)] * self.reg
grads['b{}'.format(i)] = db
loss += (self.params['W{}'.format(i)] *
self.params['W{}'.format(i)]).sum() * self.reg
############################################################################
# END OF YOUR CODE #
############################################################################
return loss, grads
def loss(self, X, y=None):
"""
Compute loss and gradient for the fully-connected net.
Input / output: Same as TwoLayerNet above.
"""
X = X.to(self.dtype)
mode = 'test' if y is None else 'train'
# Set train/test mode for batchnorm params and dropout param since they
# behave differently during training and testing.
if self.use_dropout:
self.dropout_param['mode'] = mode
scores = None
############################################################################
# TODO: Implement the forward pass for the fully-connected net, computing #
# the class scores for X and storing them in the scores variable. #
# #
# When using dropout, you'll need to pass self.dropout_param to each #
# dropout forward pass. #
############################################################################
# Replace "pass" statement with your code
if self.use_dropout:
a = [None] * (self.num_layers + 1)
cache = [None] * (self.num_layers + 1)
cache_dropout = [None] * (self.num_layers + 1)
a[0] = X
cache[0] = 0
for i in range(1,self.num_layers):
W_str = 'W{}'.format(str(i))
b_str = 'b{}'.format(str(i))
a[i], cache[i] = Linear_ReLU.forward(a[i-1], self.params[W_str], self.params[b_str])
a[i], cache_dropout[i] = Dropout.forward(a[i], self.dropout_param)
W_linear = 'W{}'.format(str(self.num_layers))
b_linear = 'b{}'.format(str(self.num_layers))
a[self.num_layers], cache[self.num_layers] = Linear.forward(a[self.num_layers-1], self.params[W_linear], self.params[b_linear])
scores = a[self.num_layers]
else:
a = [None] * (self.num_layers + 1)
cache = [None] * (self.num_layers + 1)
a[0] = X
cache[0] = 0
for i in range(1,self.num_layers):
W_str = 'W{}'.format(str(i))
b_str = 'b{}'.format(str(i))
a[i], cache[i] = Linear_ReLU.forward(a[i-1], self.params[W_str], self.params[b_str])
W_linear = 'W{}'.format(str(self.num_layers))
b_linear = 'b{}'.format(str(self.num_layers))
a[self.num_layers], cache[self.num_layers] = Linear.forward(a[self.num_layers-1], self.params[W_linear], self.params[b_linear])
scores = a[self.num_layers]
############################################################################
# END OF YOUR CODE #
############################################################################
# If test mode return early
if mode == 'test':
return scores
loss, grads = 0.0, {}
############################################################################
# TODO: Implement the backward pass for the fully-connected net. Store the #
# loss in the loss variable and gradients in the grads dictionary. Compute #
# data loss using softmax, and make sure that grads[k] holds the gradients #
# for self.params[k]. Don't forget to add L2 regularization! #
# NOTE: To ensure that your implementation matches ours and you pass the #
# automated tests, make sure that your L2 regularization includes a factor #
# of 0.5 to simplify the expression for the gradient. #
############################################################################
# Replace "pass" statement with your code
if self.use_dropout:
loss, dout_softmax = softmax_loss(scores,y)
dout = [None] * (self.num_layers + 1)
W_linear = 'W{}'.format(str(self.num_layers))
b_linear = 'b{}'.format(str(self.num_layers))
dout[self.num_layers], grads[W_linear], grads[b_linear] = Linear.backward(dout_softmax,cache[self.num_layers])
grads[W_linear] += 2 * self.reg * self.params[W_linear]
loss += self.reg * torch.sum(self.params[W_linear] * self.params[W_linear])
for i in range(self.num_layers - 1, 0, -1):
W_str = 'W{}'.format(str(i))
b_str = 'b{}'.format(str(i))
dout[i+1] = Dropout.backward(dout[i+1],cache_dropout[i])
dout[i], grads[W_str], grads[b_str] = Linear_ReLU.backward(dout[i+1],cache[i])
grads[W_str] += 2 * self.reg * self.params[W_str]
loss += self.reg * torch.sum(self.params[W_str] * self.params[W_str])
else:
loss, dout_softmax = softmax_loss(scores,y)
dout = [None] * (self.num_layers + 1)
W_linear = 'W{}'.format(str(self.num_layers))
b_linear = 'b{}'.format(str(self.num_layers))
dout[self.num_layers], grads[W_linear], grads[b_linear] = Linear.backward(dout_softmax,cache[self.num_layers])
grads[W_linear] += 2 * self.reg * self.params[W_linear]
loss += self.reg * torch.sum(self.params[W_linear] * self.params[W_linear])
for i in range(self.num_layers - 1, 0, -1):
W_str = 'W{}'.format(str(i))
b_str = 'b{}'.format(str(i))
dout[i], grads[W_str], grads[b_str] = Linear_ReLU.backward(dout[i+1],cache[i])
grads[W_str] += 2 * self.reg * self.params[W_str]
loss += self.reg * torch.sum(self.params[W_str] * self.params[W_str])
############################################################################
# END OF YOUR CODE #
############################################################################
return loss, grads
def loss(self, X, y=None):
"""
Compute loss and gradient for the fully-connected net.
Input / output: Same as TwoLayerNet above.
"""
X = X.to(self.dtype)
mode = 'test' if y is None else 'train'
# Set train/test mode for batchnorm params and dropout param since they
# behave differently during training and testing.
if self.use_dropout:
self.dropout_param['mode'] = mode
scores = None
############################################################################
# TODO: Implement the forward pass for the fully-connected net, computing #
# the class scores for X and storing them in the scores variable. #
# #
# When using dropout, you'll need to pass self.dropout_param to each #
# dropout forward pass. #
############################################################################
# Replace "pass" statement with your code
out = X
forward_cache = []
dropout_cache = []
for i in range(self.num_layers - 1):
name_W = 'W' + '{}'.format(i + 1)
name_b = 'b' + '{}'.format(i + 1)
out, cache = Linear_ReLU.forward(x=out,
w=self.params[name_W],
b=self.params[name_b])
forward_cache.append(cache)
if self.use_dropout:
out, cache = Dropout.forward(out, self.dropout_param)
dropout_cache.append(cache)
name_W = 'W' + '{}'.format(self.num_layers)
name_b = 'b' + '{}'.format(self.num_layers)
out, cache = Linear.forward(out, self.params[name_W],
self.params[name_b])
forward_cache.append(cache)
scores = out
############################################################################
# END OF YOUR CODE #
############################################################################
# If test mode return early
if mode == 'test':
return scores
loss, grads = 0.0, {}
############################################################################
# TODO: Implement the backward pass for the fully-connected net. Store the #
# loss in the loss variable and gradients in the grads dictionary. Compute #
# data loss using softmax, and make sure that grads[k] holds the gradients #
# for self.params[k]. Don't forget to add L2 regularization! #
# NOTE: To ensure that your implementation matches ours and you pass the #
# automated tests, make sure that your L2 regularization includes a factor #
# of 0.5 to simplify the expression for the gradient. #
############################################################################
# Replace "pass" statement with your code
loss, dout = softmax_loss(scores, y)
for i in range(self.num_layers):
name_W = 'W' + '{}'.format(i + 1)
loss += self.reg * torch.sum(self.params[name_W]**2)
for i in range(self.num_layers, 0, -1):
name_W = 'W' + '{}'.format(i)
name_b = 'b' + '{}'.format(i)
if i == self.num_layers:
grads_x, grads[name_W], grads[name_b] = Linear.backward(
dout, forward_cache.pop())
grads[name_W] += 2 * self.reg * self.params[name_W]
else:
if self.use_dropout:
grads_x = Dropout.backward(grads_x, dropout_cache.pop())
grads_x, grads[name_W], grads[name_b] = Linear_ReLU.backward(
grads_x, forward_cache.pop())
grads[name_W] += 2 * self.reg * self.params[name_W]
############################################################################
# END OF YOUR CODE #
############################################################################
return loss, grads
