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import numpy as np
class NN(object):
def __init__(self, hidden_dims, input_dim=32 * 32 * 3, num_classes=10, weight_scale=1e-2, learning_rate=1e-3,
reg=0.0, dtype=np.float64):
self.reg = reg
self.lr = learning_rate
self.dtype = dtype
self.params = {}
self.num_layers = len(hidden_dims) + 1
if hidden_dims is None:
self.params['W1'] = weight_scale * np.random.randn(input_dim, num_classes)
self.params['b1'] = np.zeros((1, num_classes))
else:
for i in range(self.num_layers):
if i == 0:
in_dim = input_dim
out_dim = hidden_dims[i]
elif i == (self.num_layers - 1):
in_dim = hidden_dims[i - 1]
out_dim = num_classes
else:
in_dim = hidden_dims[i - 1]
out_dim = hidden_dims[i]
self.params['W%d' % (i + 1)] = weight_scale * np.random.randn(in_dim, out_dim)
self.params['b%d' % (i + 1)] = np.zeros((1, out_dim))
self.configs = {}
config = {'learning_rate': learning_rate}
for k in self.params.keys():
self.configs[k] = config.copy()
for k, v in self.params.items():
self.params[k] = v.astype(dtype)
def train(self, X, y, num_iters=100, batch_size=200, verbose=False):
"""
Inputs:
- X: A numpy array of shape (N, D) containing training data; there are N
training samples each of dimension D.
- y: A numpy array of shape (N,) containing training labels; y[i] = c
means that X[i] has label 0 <= c < C for C classes.
- num_iters: (integer) number of steps to take when optimizing
- batch_size: (integer) number of training examples to use at each step.
- verbose: (boolean) If true, print progress during optimization.
Outputs:
A list containing the value of the loss function at each training iteration.
"""
X = X.astype(self.dtype)
num_train, dim = X.shape
loss_history = []
range_list = np.arange(0, num_train, step=batch_size)
for it in range(num_iters):
total_loss = 0
for i in range_list:
X_batch = X[i:i + batch_size]
y_batch = y[i:i + batch_size]
loss, grads = self.loss(X_batch, y_batch)
total_loss += loss
for k in self.params.keys():
w = self.params[k]
dw = grads[k]
next_w = w - self.lr * dw
self.params[k] = next_w
avg_loss = total_loss / len(range_list)
loss_history.append(avg_loss)
if verbose and it % 100 == 0:
print('iteration %d / %d: loss %f' % (it, num_iters, avg_loss))
return loss_history
def predict(self, X):
"""
Use the trained weights of this linear classifier to predict labels for
data points.
Inputs:
- X: A numpy array of shape (N, D) containing training data; there are N
training samples each of dimension D.
Returns:
- y_pred: Predicted labels for the data in X. y_pred is a 1-dimensional
array of length N, and each element is an integer giving the predicted
class.
"""
scores, caches = self.forward(X)
scores -= np.atleast_2d(np.max(scores, axis=1)).T
exp_scores = np.exp(scores)
probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
y_pred = np.argmax(probs, axis=1)
return y_pred
def loss(self, X_batch, y_batch):
"""
Compute the loss function and its derivative.
Subclasses will override this.
Inputs:
- X_batch: A numpy array of shape (N, D) containing a minibatch of N
data points; each point has dimension D.
- y_batch: A numpy array of shape (N,) containing labels for the minibatch.
Returns: A tuple containing:
- loss as a single float
- gradient with respect to self.W; an array of the same shape as W
"""
scores, caches = self.forward(X_batch)
data_loss, dout = self.softmax_loss(scores, y_batch)
reg_loss = 0
for i in range(self.num_layers):
reg_loss += 0.5 * self.reg * np.sum(self.params['W%d' % (i + 1)] ** 2)
loss = data_loss + reg_loss
grads = {}
dx = None
for i in reversed(range(self.num_layers)):
cache = caches['cache%d' % (i + 1)]
if i == (self.num_layers - 1):
dx, dw, db = self.affine_backward(dout, cache)
else:
dx, dw, db = self.affine_relu_backward(dx, cache)
grads['W%d' % (i + 1)] = dw + self.reg * self.params['W%d' % (i + 1)]
grads['b%d' % (i + 1)] = db
return loss, grads
def forward(self, X):
a = None
z = None
caches = {}
for i in range(self.num_layers):
if i == 0:
a = X
if i == (self.num_layers - 1):
z, caches['cache%d' % self.num_layers] = self.affine_forward(a,
self.params['W%d' % (self.num_layers)],
self.params['b%d' % (self.num_layers)])
else:
a, caches['cache%d' % (i + 1)] = self.affine_relu_forward(a,
self.params['W%d' % (i + 1)],
self.params['b%d' % (i + 1)])
scores = z
return scores, caches
def affine_relu_forward(self, x, w, b):
"""
Convenience layer that perorms an affine transform followed by a ReLU
Inputs:
- x: Input to the affine layer
- w, b: Weights for the affine layer
Returns a tuple of:
- out: Output from the ReLU
- cache: Object to give to the backward pass
"""
a, fc_cache = self.affine_forward(x, w, b)
out, relu_cache = self.relu_forward(a)
cache = (fc_cache, relu_cache)
return out, cache
def affine_relu_backward(self, dout, cache):
"""
Backward pass for the affine-relu convenience layer
"""
fc_cache, relu_cache = cache
da = self.relu_backward(dout, relu_cache)
dx, dw, db = self.affine_backward(da, fc_cache)
return dx, dw, db
def affine_forward(self, x, w, b):
"""
Computes the forward pass for an affine (fully-connected) layer.
The input x has shape (N, d_1, ..., d_k) and contains a minibatch of N
examples, where each example x[i] has shape (d_1, ..., d_k). We will
reshape each input into a vector of dimension D = d_1 * ... * d_k, and
then transform it to an output vector of dimension M.
Inputs:
- x: A numpy array containing input data, of shape (N, d_1, ..., d_k)
- w: A numpy array of weights, of shape (D, M)
- b: A numpy array of biases, of shape (M,)
Returns a tuple of:
- out: output, of shape (N, M)
- cache: (x, w, b)
"""
inputs = x.reshape(x.shape[0], -1)
out = inputs.dot(w) + b.reshape(1, -1)
cache = (x, w, b)
return out, cache
def affine_backward(self, dout, cache):
"""
Computes the backward pass for an affine layer.
Inputs:
- dout: Upstream derivative, of shape (N, M)
- cache: Tuple of:
- x: Input data, of shape (N, d_1, ... d_k)
- w: Weights, of shape (D, M)
- b: Biases, of shape (M,)
Returns a tuple of:
- dx: Gradient with respect to x, of shape (N, d1, ..., d_k)
- dw: Gradient with respect to w, of shape (D, M)
- db: Gradient with respect to b, of shape (M,)
"""
x, w, b = cache
dx = dout.dot(w.T).reshape(x.shape)
dw = x.reshape(x.shape[0], -1).T.dot(dout)
db = np.sum(dout, axis=0)
return dx, dw, db
def relu_forward(self, x):
"""
Computes the forward pass for a layer of rectified linear units (ReLUs).
Input:
- x: Inputs, of any shape
Returns a tuple of:
- out: Output, of the same shape as x
- cache: x
"""
out = x.copy()
out[x < 0] = 0
cache = x
return out, cache
def relu_backward(self, dout, cache):
"""
Computes the backward pass for a layer of rectified linear units (ReLUs).
Input:
- dout: Upstream derivatives, of any shape
- cache: Input x, of same shape as dout
Returns:
- dx: Gradient with respect to x
"""
dx, x = None, cache
dx = dout
dx[x < 0] = 0
return dx
def softmax_loss(self, scores, y):
num = y.shape[0]
exp_scores = np.exp(scores)
probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
data_loss = -1.0 / num * np.sum(np.log(probs[range(num), y]))
dscores = scores
dscores[range(num), y] -= 1
dscores /= num
return data_loss, dscores
def adam(self, w, dw, config=None):
"""
Uses the Adam update rule, which incorporates moving averages of both the
gradient and its square and a bias correction term.
config format:
- learning_rate: Scalar learning rate.
- beta1: Decay rate for moving average of first moment of gradient.
- beta2: Decay rate for moving average of second moment of gradient.
- epsilon: Small scalar used for smoothing to avoid dividing by zero.
- m: Moving average of gradient.
- v: Moving average of squared gradient.
- t: Iteration number.
"""
if config is None: config = {}
config.setdefault('learning_rate', 1e-3)
config.setdefault('beta1', 0.9)
config.setdefault('beta2', 0.999)
config.setdefault('epsilon', 1e-8)
config.setdefault('m', np.zeros_like(w))
config.setdefault('v', np.zeros_like(w))
config.setdefault('t', 0)
t = config['t'] + 1
m = config['beta1'] * config['m'] + (1 - config['beta1']) * dw
mt = m / (1 - config['beta1'] ** t)
v = config['beta2'] * config['v'] + (1 - config['beta2']) * (dw ** 2)
vt = v / (1 - config['beta2'] ** t)
next_w = w - config['learning_rate'] * mt / (np.sqrt(vt) + config['epsilon'])
config['t'] = t
config['m'] = m
config['v'] = v
return next_w, config
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