mirror of https://github.com/da0c/DL_Course_SamU
add assignment 2
Viktoria Kutikova
4 years ago
parent
3ec44aecf0
commit
e24d001813
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from scripts.classifiers.k_nearest_neighbor import *
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from scripts.classifiers.linear_classifier import *
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from builtins import range
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from builtins import object
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import numpy as np
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from past.builtins import xrange
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class KNearestNeighbor(object):
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""" a kNN classifier with L2 distance """
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def __init__(self):
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pass
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def train(self, X, y):
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"""
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Train the classifier. For k-nearest neighbors this is just
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memorizing the training data.
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Inputs:
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- X: A numpy array of shape (num_train, D) containing the training data
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consisting of num_train samples each of dimension D.
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- y: A numpy array of shape (N,) containing the training labels, where
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y[i] is the label for X[i].
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"""
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self.X_train = X
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self.y_train = y
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def predict(self, X, k=1, num_loops=0):
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"""
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Predict labels for test data using this classifier.
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Inputs:
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- X: A numpy array of shape (num_test, D) containing test data consisting
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of num_test samples each of dimension D.
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- k: The number of nearest neighbors that vote for the predicted labels.
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- num_loops: Determines which implementation to use to compute distances
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between training points and testing points.
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Returns:
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- y: A numpy array of shape (num_test,) containing predicted labels for the
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test data, where y[i] is the predicted label for the test point X[i].
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"""
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if num_loops == 0:
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dists = self.compute_distances_no_loops(X)
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elif num_loops == 1:
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dists = self.compute_distances_one_loop(X)
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elif num_loops == 2:
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dists = self.compute_distances_two_loops(X)
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else:
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raise ValueError('Invalid value %d for num_loops' % num_loops)
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return self.predict_labels(dists, k=k)
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def compute_distances_two_loops(self, X):
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"""
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Compute the distance between each test point in X and each training point
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in self.X_train using a nested loop over both the training data and the
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test data.
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Inputs:
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- X: A numpy array of shape (num_test, D) containing test data.
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Returns:
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- dists: A numpy array of shape (num_test, num_train) where dists[i, j]
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is the Euclidean distance between the ith test point and the jth training
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point.
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"""
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num_test = X.shape[0]
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num_train = self.X_train.shape[0]
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dists = np.zeros((num_test, num_train))
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for i in range(num_test):
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for j in range(num_train):
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#####################################################################
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# TODO: #
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# Compute the l2 distance between the ith test point and the jth #
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# training point, and store the result in dists[i, j]. You should #
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# not use a loop over dimension, nor use np.linalg.norm(). #
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#####################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return dists
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def compute_distances_one_loop(self, X):
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"""
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Compute the distance between each test point in X and each training point
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in self.X_train using a single loop over the test data.
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Input / Output: Same as compute_distances_two_loops
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"""
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num_test = X.shape[0]
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num_train = self.X_train.shape[0]
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dists = np.zeros((num_test, num_train))
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for i in range(num_test):
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#######################################################################
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# TODO: #
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# Compute the l2 distance between the ith test point and all training #
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# points, and store the result in dists[i, :]. #
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# Do not use np.linalg.norm(). #
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#######################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return dists
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def compute_distances_no_loops(self, X):
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"""
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Compute the distance between each test point in X and each training point
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in self.X_train using no explicit loops.
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Input / Output: Same as compute_distances_two_loops
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"""
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num_test = X.shape[0]
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num_train = self.X_train.shape[0]
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dists = np.zeros((num_test, num_train))
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#########################################################################
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# TODO: #
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# Compute the l2 distance between all test points and all training #
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# points without using any explicit loops, and store the result in #
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# dists. #
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# #
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# You should implement this function using only basic array operations; #
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# in particular you should not use functions from scipy, #
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# nor use np.linalg.norm(). #
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# #
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# HINT: Try to formulate the l2 distance using matrix multiplication #
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# and two broadcast sums. #
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#########################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return dists
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def predict_labels(self, dists, k=1):
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"""
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Given a matrix of distances between test points and training points,
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predict a label for each test point.
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Inputs:
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- dists: A numpy array of shape (num_test, num_train) where dists[i, j]
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gives the distance betwen the ith test point and the jth training point.
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Returns:
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- y: A numpy array of shape (num_test,) containing predicted labels for the
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test data, where y[i] is the predicted label for the test point X[i].
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"""
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num_test = dists.shape[0]
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y_pred = np.zeros(num_test)
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for i in range(num_test):
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# A list of length k storing the labels of the k nearest neighbors to
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# the ith test point.
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closest_y = []
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#########################################################################
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# TODO: #
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# Use the distance matrix to find the k nearest neighbors of the ith #
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# testing point, and use self.y_train to find the labels of these #
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# neighbors. Store these labels in closest_y. #
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# Hint: Look up the function numpy.argsort. #
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#########################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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#########################################################################
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# TODO: #
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# Now that you have found the labels of the k nearest neighbors, you #
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# need to find the most common label in the list closest_y of labels. #
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# Store this label in y_pred[i]. Break ties by choosing the smaller #
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# label. #
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#########################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return y_pred
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from __future__ import print_function
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from builtins import range
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from builtins import object
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import numpy as np
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from scripts.classifiers.linear_svm import *
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from scripts.classifiers.softmax import *
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from past.builtins import xrange
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class LinearClassifier(object):
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def __init__(self):
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self.W = None
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def train(self, X, y, learning_rate=1e-3, reg=1e-5, num_iters=100,
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batch_size=200, verbose=False):
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"""
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Train this linear classifier using stochastic gradient descent.
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Inputs:
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- X: A numpy array of shape (N, D) containing training data; there are N
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training samples each of dimension D.
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- y: A numpy array of shape (N,) containing training labels; y[i] = c
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means that X[i] has label 0 <= c < C for C classes.
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- learning_rate: (float) learning rate for optimization.
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- reg: (float) regularization strength.
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- num_iters: (integer) number of steps to take when optimizing
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- batch_size: (integer) number of training examples to use at each step.
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- verbose: (boolean) If true, print progress during optimization.
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Outputs:
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A list containing the value of the loss function at each training iteration.
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"""
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num_train, dim = X.shape
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num_classes = np.max(y) + 1 # assume y takes values 0...K-1 where K is number of classes
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if self.W is None:
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# lazily initialize W
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self.W = 0.001 * np.random.randn(dim, num_classes)
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# Run stochastic gradient descent to optimize W
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loss_history = []
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for it in range(num_iters):
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X_batch = None
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y_batch = None
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#########################################################################
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# TODO: #
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# Sample batch_size elements from the training data and their #
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# corresponding labels to use in this round of gradient descent. #
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# Store the data in X_batch and their corresponding labels in #
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# y_batch; after sampling X_batch should have shape (batch_size, dim) #
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# and y_batch should have shape (batch_size,) #
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# #
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# Hint: Use np.random.choice to generate indices. Sampling with #
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# replacement is faster than sampling without replacement. #
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#########################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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# evaluate loss and gradient
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loss, grad = self.loss(X_batch, y_batch, reg)
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loss_history.append(loss)
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# perform parameter update
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#########################################################################
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# TODO: #
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# Update the weights using the gradient and the learning rate. #
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#########################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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if verbose and it % 100 == 0:
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print('iteration %d / %d: loss %f' % (it, num_iters, loss))
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return loss_history
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def predict(self, X):
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"""
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Use the trained weights of this linear classifier to predict labels for
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data points.
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Inputs:
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- X: A numpy array of shape (N, D) containing training data; there are N
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training samples each of dimension D.
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Returns:
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- y_pred: Predicted labels for the data in X. y_pred is a 1-dimensional
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array of length N, and each element is an integer giving the predicted
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class.
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"""
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y_pred = np.zeros(X.shape[0])
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###########################################################################
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# TODO: #
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# Implement this method. Store the predicted labels in y_pred. #
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###########################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return y_pred
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def loss(self, X_batch, y_batch, reg):
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"""
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Compute the loss function and its derivative.
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Subclasses will override this.
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Inputs:
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- X_batch: A numpy array of shape (N, D) containing a minibatch of N
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data points; each point has dimension D.
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- y_batch: A numpy array of shape (N,) containing labels for the minibatch.
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- reg: (float) regularization strength.
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Returns: A tuple containing:
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- loss as a single float
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- gradient with respect to self.W; an array of the same shape as W
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"""
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pass
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class LinearSVM(LinearClassifier):
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""" A subclass that uses the Multiclass SVM loss function """
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def loss(self, X_batch, y_batch, reg):
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return svm_loss_vectorized(self.W, X_batch, y_batch, reg)
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class Softmax(LinearClassifier):
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""" A subclass that uses the Softmax + Cross-entropy loss function """
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def loss(self, X_batch, y_batch, reg):
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return softmax_loss_vectorized(self.W, X_batch, y_batch, reg)
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from builtins import range
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import numpy as np
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from random import shuffle
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from past.builtins import xrange
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def svm_loss_naive(W, X, y, reg):
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"""
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Structured SVM loss function, naive implementation (with loops).
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Inputs have dimension D, there are C classes, and we operate on minibatches
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of N examples.
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Inputs:
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- W: A numpy array of shape (D, C) containing weights.
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- X: A numpy array of shape (N, D) containing a minibatch of data.
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- y: A numpy array of shape (N,) containing training labels; y[i] = c means
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that X[i] has label c, where 0 <= c < C.
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- reg: (float) regularization strength
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Returns a tuple of:
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- loss as single float
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- gradient with respect to weights W; an array of same shape as W
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"""
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dW = np.zeros(W.shape) # initialize the gradient as zero
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# compute the loss and the gradient
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num_classes = W.shape[1]
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num_train = X.shape[0]
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loss = 0.0
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for i in range(num_train):
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scores = X[i].dot(W)
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correct_class_score = scores[y[i]]
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for j in range(num_classes):
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if j == y[i]:
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continue
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margin = scores[j] - correct_class_score + 1 # note delta = 1
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if margin > 0:
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loss += margin
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# Right now the loss is a sum over all training examples, but we want it
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# to be an average instead so we divide by num_train.
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loss /= num_train
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# Add regularization to the loss.
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loss += reg * np.sum(W * W)
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#############################################################################
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# TODO: #
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# Compute the gradient of the loss function and store it dW. #
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# Rather than first computing the loss and then computing the derivative, #
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# it may be simpler to compute the derivative at the same time that the #
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# loss is being computed. As a result you may need to modify some of the #
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# code above to compute the gradient. #
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#############################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return loss, dW
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def svm_loss_vectorized(W, X, y, reg):
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"""
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Structured SVM loss function, vectorized implementation.
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Inputs and outputs are the same as svm_loss_naive.
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"""
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loss = 0.0
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dW = np.zeros(W.shape) # initialize the gradient as zero
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#############################################################################
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# TODO: #
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# Implement a vectorized version of the structured SVM loss, storing the #
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# result in loss. #
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#############################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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#############################################################################
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# TODO: #
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# Implement a vectorized version of the gradient for the structured SVM #
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# loss, storing the result in dW. #
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# #
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# Hint: Instead of computing the gradient from scratch, it may be easier #
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# to reuse some of the intermediate values that you used to compute the #
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# loss. #
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#############################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return loss, dW
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from __future__ import print_function
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from builtins import range
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from builtins import object
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import numpy as np
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import matplotlib.pyplot as plt
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from past.builtins import xrange
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class TwoLayerNet(object):
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"""
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A two-layer fully-connected neural network. The net has an input dimension of
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N, a hidden layer dimension of H, and performs classification over C classes.
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We train the network with a softmax loss function and L2 regularization on the
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weight matrices. The network uses a ReLU nonlinearity after the first fully
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connected layer.
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In other words, the network has the following architecture:
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input - fully connected layer - ReLU - fully connected layer - softmax
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The outputs of the second fully-connected layer are the scores for each class.
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"""
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def __init__(self, input_size, hidden_size, output_size, std=1e-4):
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"""
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Initialize the model. Weights are initialized to small random values and
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biases are initialized to zero. Weights and biases are stored in the
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variable self.params, which is a dictionary with the following keys:
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W1: First layer weights; has shape (D, H)
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b1: First layer biases; has shape (H,)
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W2: Second layer weights; has shape (H, C)
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b2: Second layer biases; has shape (C,)
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Inputs:
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- input_size: The dimension D of the input data.
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- hidden_size: The number of neurons H in the hidden layer.
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- output_size: The number of classes C.
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"""
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self.params = {}
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self.params['W1'] = std * np.random.randn(input_size, hidden_size)
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self.params['b1'] = np.zeros(hidden_size)
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self.params['W2'] = std * np.random.randn(hidden_size, output_size)
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self.params['b2'] = np.zeros(output_size)
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def loss(self, X, y=None, reg=0.0):
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"""
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Compute the loss and gradients for a two layer fully connected neural
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network.
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Inputs:
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- X: Input data of shape (N, D). Each X[i] is a training sample.
|
||||
- y: Vector of training labels. y[i] is the label for X[i], and each y[i] is
|
||||
an integer in the range 0 <= y[i] < C. This parameter is optional; if it
|
||||
is not passed then we only return scores, and if it is passed then we
|
||||
instead return the loss and gradients.
|
||||
- reg: Regularization strength.
|
||||
|
||||
Returns:
|
||||
If y is None, return a matrix scores of shape (N, C) where scores[i, c] is
|
||||
the score for class c on input X[i].
|
||||
|
||||
If y is not None, instead return a tuple of:
|
||||
- loss: Loss (data loss and regularization loss) for this batch of training
|
||||
samples.
|
||||
- grads: Dictionary mapping parameter names to gradients of those parameters
|
||||
with respect to the loss function; has the same keys as self.params.
|
||||
"""
|
||||
# Unpack variables from the params dictionary
|
||||
W1, b1 = self.params['W1'], self.params['b1']
|
||||
W2, b2 = self.params['W2'], self.params['b2']
|
||||
N, D = X.shape
|
||||
|
||||
# Compute the forward pass
|
||||
scores = None
|
||||
#############################################################################
|
||||
# TODO: Perform the forward pass, computing the class scores for the input. #
|
||||
# Store the result in the scores variable, which should be an array of #
|
||||
# shape (N, C). #
|
||||
#############################################################################
|
||||
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
pass
|
||||
|
||||
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
# If the targets are not given then jump out, we're done
|
||||
if y is None:
|
||||
return scores
|
||||
|
||||
# Compute the loss
|
||||
loss = None
|
||||
#############################################################################
|
||||
# TODO: Finish the forward pass, and compute the loss. This should include #
|
||||
# both the data loss and L2 regularization for W1 and W2. Store the result #
|
||||
# in the variable loss, which should be a scalar. Use the Softmax #
|
||||
# classifier loss. #
|
||||
#############################################################################
|
||||
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
pass
|
||||
|
||||
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
# Backward pass: compute gradients
|
||||
grads = {}
|
||||
#############################################################################
|
||||
# TODO: Compute the backward pass, computing the derivatives of the weights #
|
||||
# and biases. Store the results in the grads dictionary. For example, #
|
||||
# grads['W1'] should store the gradient on W1, and be a matrix of same size #
|
||||
#############################################################################
|
||||
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
pass
|
||||
|
||||
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
return loss, grads
|
||||
|
||||
def train(self, X, y, X_val, y_val,
|
||||
learning_rate=1e-3, learning_rate_decay=0.95,
|
||||
reg=5e-6, num_iters=100,
|
||||
batch_size=200, verbose=False):
|
||||
"""
|
||||
Train this neural network using stochastic gradient descent.
|
||||
|
||||
Inputs:
|
||||
- X: A numpy array of shape (N, D) giving training data.
|
||||
- y: A numpy array f shape (N,) giving training labels; y[i] = c means that
|
||||
X[i] has label c, where 0 <= c < C.
|
||||
- X_val: A numpy array of shape (N_val, D) giving validation data.
|
||||
- y_val: A numpy array of shape (N_val,) giving validation labels.
|
||||
- learning_rate: Scalar giving learning rate for optimization.
|
||||
- learning_rate_decay: Scalar giving factor used to decay the learning rate
|
||||
after each epoch.
|
||||
- reg: Scalar giving regularization strength.
|
||||
- num_iters: Number of steps to take when optimizing.
|
||||
- batch_size: Number of training examples to use per step.
|
||||
- verbose: boolean; if true print progress during optimization.
|
||||
"""
|
||||
num_train = X.shape[0]
|
||||
iterations_per_epoch = max(num_train / batch_size, 1)
|
||||
|
||||
# Use SGD to optimize the parameters in self.model
|
||||
loss_history = []
|
||||
train_acc_history = []
|
||||
val_acc_history = []
|
||||
|
||||
for it in range(num_iters):
|
||||
X_batch = None
|
||||
y_batch = None
|
||||
|
||||
#########################################################################
|
||||
# TODO: Create a random minibatch of training data and labels, storing #
|
||||
# them in X_batch and y_batch respectively. #
|
||||
#########################################################################
|
||||
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
pass
|
||||
|
||||
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
# Compute loss and gradients using the current minibatch
|
||||
loss, grads = self.loss(X_batch, y=y_batch, reg=reg)
|
||||
loss_history.append(loss)
|
||||
|
||||
#########################################################################
|
||||
# TODO: Use the gradients in the grads dictionary to update the #
|
||||
# parameters of the network (stored in the dictionary self.params) #
|
||||
# using stochastic gradient descent. You'll need to use the gradients #
|
||||
# stored in the grads dictionary defined above. #
|
||||
#########################################################################
|
||||
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
pass
|
||||
|
||||
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
if verbose and it % 100 == 0:
|
||||
print('iteration %d / %d: loss %f' % (it, num_iters, loss))
|
||||
|
||||
# Every epoch, check train and val accuracy and decay learning rate.
|
||||
if it % iterations_per_epoch == 0:
|
||||
# Check accuracy
|
||||
train_acc = (self.predict(X_batch) == y_batch).mean()
|
||||
val_acc = (self.predict(X_val) == y_val).mean()
|
||||
train_acc_history.append(train_acc)
|
||||
val_acc_history.append(val_acc)
|
||||
|
||||
# Decay learning rate
|
||||
learning_rate *= learning_rate_decay
|
||||
|
||||
return {
|
||||
'loss_history': loss_history,
|
||||
'train_acc_history': train_acc_history,
|
||||
'val_acc_history': val_acc_history,
|
||||
}
|
||||
|
||||
def predict(self, X):
|
||||
"""
|
||||
Use the trained weights of this two-layer network to predict labels for
|
||||
data points. For each data point we predict scores for each of the C
|
||||
classes, and assign each data point to the class with the highest score.
|
||||
|
||||
Inputs:
|
||||
- X: A numpy array of shape (N, D) giving N D-dimensional data points to
|
||||
classify.
|
||||
|
||||
Returns:
|
||||
- y_pred: A numpy array of shape (N,) giving predicted labels for each of
|
||||
the elements of X. For all i, y_pred[i] = c means that X[i] is predicted
|
||||
to have class c, where 0 <= c < C.
|
||||
"""
|
||||
y_pred = None
|
||||
|
||||
###########################################################################
|
||||
# TODO: Implement this function; it should be VERY simple! #
|
||||
###########################################################################
|
||||
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
pass
|
||||
|
||||
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
return y_pred
|
||||
@ -0,0 +1,65 @@
|
||||
from builtins import range
|
||||
import numpy as np
|
||||
from random import shuffle
|
||||
from past.builtins import xrange
|
||||
|
||||
def softmax_loss_naive(W, X, y, reg):
|
||||
"""
|
||||
Softmax loss function, naive implementation (with loops)
|
||||
|
||||
Inputs have dimension D, there are C classes, and we operate on minibatches
|
||||
of N examples.
|
||||
|
||||
Inputs:
|
||||
- W: A numpy array of shape (D, C) containing weights.
|
||||
- X: A numpy array of shape (N, D) containing a minibatch of data.
|
||||
- y: A numpy array of shape (N,) containing training labels; y[i] = c means
|
||||
that X[i] has label c, where 0 <= c < C.
|
||||
- reg: (float) regularization strength
|
||||
|
||||
Returns a tuple of:
|
||||
- loss as single float
|
||||
- gradient with respect to weights W; an array of same shape as W
|
||||
"""
|
||||
# Initialize the loss and gradient to zero.
|
||||
loss = 0.0
|
||||
dW = np.zeros_like(W)
|
||||
|
||||
#############################################################################
|
||||
# TODO: Compute the softmax loss and its gradient using explicit loops. #
|
||||
# Store the loss in loss and the gradient in dW. If you are not careful #
|
||||
# here, it is easy to run into numeric instability. Don't forget the #
|
||||
# regularization! #
|
||||
#############################################################################
|
||||
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
pass
|
||||
|
||||
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
return loss, dW
|
||||
|
||||
|
||||
def softmax_loss_vectorized(W, X, y, reg):
|
||||
"""
|
||||
Softmax loss function, vectorized version.
|
||||
|
||||
Inputs and outputs are the same as softmax_loss_naive.
|
||||
"""
|
||||
# Initialize the loss and gradient to zero.
|
||||
loss = 0.0
|
||||
dW = np.zeros_like(W)
|
||||
|
||||
#############################################################################
|
||||
# TODO: Compute the softmax loss and its gradient using no explicit loops. #
|
||||
# Store the loss in loss and the gradient in dW. If you are not careful #
|
||||
# here, it is easy to run into numeric instability. Don't forget the #
|
||||
# regularization! #
|
||||
#############################################################################
|
||||
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
pass
|
||||
|
||||
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
|
||||
|
||||
return loss, dW
|
||||
@ -0,0 +1,262 @@
|
||||
from __future__ import print_function
|
||||
|
||||
from builtins import range
|
||||
from six.moves import cPickle as pickle
|
||||
import numpy as np
|
||||
import os
|
||||
from imageio import imread
|
||||
import platform
|
||||
|
||||
def load_pickle(f):
|
||||
version = platform.python_version_tuple()
|
||||
if version[0] == '2':
|
||||
return pickle.load(f)
|
||||
elif version[0] == '3':
|
||||
return pickle.load(f, encoding='latin1')
|
||||
raise ValueError("invalid python version: {}".format(version))
|
||||
|
||||
def load_CIFAR_batch(filename):
|
||||
""" load single batch of cifar """
|
||||
with open(filename, 'rb') as f:
|
||||
datadict = load_pickle(f)
|
||||
X = datadict['data']
|
||||
Y = datadict['labels']
|
||||
X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")
|
||||
Y = np.array(Y)
|
||||
return X, Y
|
||||
|
||||
def load_CIFAR10(ROOT):
|
||||
""" load all of cifar """
|
||||
xs = []
|
||||
ys = []
|
||||
for b in range(1,6):
|
||||
f = os.path.join(ROOT, 'data_batch_%d' % (b, ))
|
||||
X, Y = load_CIFAR_batch(f)
|
||||
xs.append(X)
|
||||
ys.append(Y)
|
||||
Xtr = np.concatenate(xs)
|
||||
Ytr = np.concatenate(ys)
|
||||
del X, Y
|
||||
Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
|
||||
return Xtr, Ytr, Xte, Yte
|
||||
|
||||
|
||||
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000,
|
||||
subtract_mean=True):
|
||||
"""
|
||||
Load the CIFAR-10 dataset from disk and perform preprocessing to prepare
|
||||
it for classifiers. These are the same steps as we used for the SVM, but
|
||||
condensed to a single function.
|
||||
"""
|
||||
# Load the raw CIFAR-10 data
|
||||
cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'
|
||||
X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)
|
||||
|
||||
# Subsample the data
|
||||
mask = list(range(num_training, num_training + num_validation))
|
||||
X_val = X_train[mask]
|
||||
y_val = y_train[mask]
|
||||
mask = list(range(num_training))
|
||||
X_train = X_train[mask]
|
||||
y_train = y_train[mask]
|
||||
mask = list(range(num_test))
|
||||
X_test = X_test[mask]
|
||||
y_test = y_test[mask]
|
||||
|
||||
# Normalize the data: subtract the mean image
|
||||
if subtract_mean:
|
||||
mean_image = np.mean(X_train, axis=0)
|
||||
X_train -= mean_image
|
||||
X_val -= mean_image
|
||||
X_test -= mean_image
|
||||
|
||||
# Transpose so that channels come first
|
||||
X_train = X_train.transpose(0, 3, 1, 2).copy()
|
||||
X_val = X_val.transpose(0, 3, 1, 2).copy()
|
||||
X_test = X_test.transpose(0, 3, 1, 2).copy()
|
||||
|
||||
# Package data into a dictionary
|
||||
return {
|
||||
'X_train': X_train, 'y_train': y_train,
|
||||
'X_val': X_val, 'y_val': y_val,
|
||||
'X_test': X_test, 'y_test': y_test,
|
||||
}
|
||||
|
||||
|
||||
def load_tiny_imagenet(path, dtype=np.float32, subtract_mean=True):
|
||||
"""
|
||||
Load TinyImageNet. Each of TinyImageNet-100-A, TinyImageNet-100-B, and
|
||||
TinyImageNet-200 have the same directory structure, so this can be used
|
||||
to load any of them.
|
||||
|
||||
Inputs:
|
||||
- path: String giving path to the directory to load.
|
||||
- dtype: numpy datatype used to load the data.
|
||||
- subtract_mean: Whether to subtract the mean training image.
|
||||
|
||||
Returns: A dictionary with the following entries:
|
||||
- class_names: A list where class_names[i] is a list of strings giving the
|
||||
WordNet names for class i in the loaded dataset.
|
||||
- X_train: (N_tr, 3, 64, 64) array of training images
|
||||
- y_train: (N_tr,) array of training labels
|
||||
- X_val: (N_val, 3, 64, 64) array of validation images
|
||||
- y_val: (N_val,) array of validation labels
|
||||
- X_test: (N_test, 3, 64, 64) array of testing images.
|
||||
- y_test: (N_test,) array of test labels; if test labels are not available
|
||||
(such as in student code) then y_test will be None.
|
||||
- mean_image: (3, 64, 64) array giving mean training image
|
||||
"""
|
||||
# First load wnids
|
||||
with open(os.path.join(path, 'wnids.txt'), 'r') as f:
|
||||
wnids = [x.strip() for x in f]
|
||||
|
||||
# Map wnids to integer labels
|
||||
wnid_to_label = {wnid: i for i, wnid in enumerate(wnids)}
|
||||
|
||||
# Use words.txt to get names for each class
|
||||
with open(os.path.join(path, 'words.txt'), 'r') as f:
|
||||
wnid_to_words = dict(line.split('\t') for line in f)
|
||||
for wnid, words in wnid_to_words.items():
|
||||
wnid_to_words[wnid] = [w.strip() for w in words.split(',')]
|
||||
class_names = [wnid_to_words[wnid] for wnid in wnids]
|
||||
|
||||
# Next load training data.
|
||||
X_train = []
|
||||
y_train = []
|
||||
for i, wnid in enumerate(wnids):
|
||||
if (i + 1) % 20 == 0:
|
||||
print('loading training data for synset %d / %d'
|
||||
% (i + 1, len(wnids)))
|
||||
# To figure out the filenames we need to open the boxes file
|
||||
boxes_file = os.path.join(path, 'train', wnid, '%s_boxes.txt' % wnid)
|
||||
with open(boxes_file, 'r') as f:
|
||||
filenames = [x.split('\t')[0] for x in f]
|
||||
num_images = len(filenames)
|
||||
|
||||
X_train_block = np.zeros((num_images, 3, 64, 64), dtype=dtype)
|
||||
y_train_block = wnid_to_label[wnid] * \
|
||||
np.ones(num_images, dtype=np.int64)
|
||||
for j, img_file in enumerate(filenames):
|
||||
img_file = os.path.join(path, 'train', wnid, 'images', img_file)
|
||||
img = imread(img_file)
|
||||
if img.ndim == 2:
|
||||
## grayscale file
|
||||
img.shape = (64, 64, 1)
|
||||
X_train_block[j] = img.transpose(2, 0, 1)
|
||||
X_train.append(X_train_block)
|
||||
y_train.append(y_train_block)
|
||||
|
||||
# We need to concatenate all training data
|
||||
X_train = np.concatenate(X_train, axis=0)
|
||||
y_train = np.concatenate(y_train, axis=0)
|
||||
|
||||
# Next load validation data
|
||||
with open(os.path.join(path, 'val', 'val_annotations.txt'), 'r') as f:
|
||||
img_files = []
|
||||
val_wnids = []
|
||||
for line in f:
|
||||
img_file, wnid = line.split('\t')[:2]
|
||||
img_files.append(img_file)
|
||||
val_wnids.append(wnid)
|
||||
num_val = len(img_files)
|
||||
y_val = np.array([wnid_to_label[wnid] for wnid in val_wnids])
|
||||
X_val = np.zeros((num_val, 3, 64, 64), dtype=dtype)
|
||||
for i, img_file in enumerate(img_files):
|
||||
img_file = os.path.join(path, 'val', 'images', img_file)
|
||||
img = imread(img_file)
|
||||
if img.ndim == 2:
|
||||
img.shape = (64, 64, 1)
|
||||
X_val[i] = img.transpose(2, 0, 1)
|
||||
|
||||
# Next load test images
|
||||
# Students won't have test labels, so we need to iterate over files in the
|
||||
# images directory.
|
||||
img_files = os.listdir(os.path.join(path, 'test', 'images'))
|
||||
X_test = np.zeros((len(img_files), 3, 64, 64), dtype=dtype)
|
||||
for i, img_file in enumerate(img_files):
|
||||
img_file = os.path.join(path, 'test', 'images', img_file)
|
||||
img = imread(img_file)
|
||||
if img.ndim == 2:
|
||||
img.shape = (64, 64, 1)
|
||||
X_test[i] = img.transpose(2, 0, 1)
|
||||
|
||||
y_test = None
|
||||
y_test_file = os.path.join(path, 'test', 'test_annotations.txt')
|
||||
if os.path.isfile(y_test_file):
|
||||
with open(y_test_file, 'r') as f:
|
||||
img_file_to_wnid = {}
|
||||
for line in f:
|
||||
line = line.split('\t')
|
||||
img_file_to_wnid[line[0]] = line[1]
|
||||
y_test = [wnid_to_label[img_file_to_wnid[img_file]]
|
||||
for img_file in img_files]
|
||||
y_test = np.array(y_test)
|
||||
|
||||
mean_image = X_train.mean(axis=0)
|
||||
if subtract_mean:
|
||||
X_train -= mean_image[None]
|
||||
X_val -= mean_image[None]
|
||||
X_test -= mean_image[None]
|
||||
|
||||
return {
|
||||
'class_names': class_names,
|
||||
'X_train': X_train,
|
||||
'y_train': y_train,
|
||||
'X_val': X_val,
|
||||
'y_val': y_val,
|
||||
'X_test': X_test,
|
||||
'y_test': y_test,
|
||||
'class_names': class_names,
|
||||
'mean_image': mean_image,
|
||||
}
|
||||
|
||||
|
||||
def load_models(models_dir):
|
||||
"""
|
||||
Load saved models from disk. This will attempt to unpickle all files in a
|
||||
directory; any files that give errors on unpickling (such as README.txt)
|
||||
will be skipped.
|
||||
|
||||
Inputs:
|
||||
- models_dir: String giving the path to a directory containing model files.
|
||||
Each model file is a pickled dictionary with a 'model' field.
|
||||
|
||||
Returns:
|
||||
A dictionary mapping model file names to models.
|
||||
"""
|
||||
models = {}
|
||||
for model_file in os.listdir(models_dir):
|
||||
with open(os.path.join(models_dir, model_file), 'rb') as f:
|
||||
try:
|
||||
models[model_file] = load_pickle(f)['model']
|
||||
except pickle.UnpicklingError:
|
||||
continue
|
||||
return models
|
||||
|
||||
|
||||
def load_imagenet_val(num=None):
|
||||
"""Load a handful of validation images from ImageNet.
|
||||
|
||||
Inputs:
|
||||
- num: Number of images to load (max of 25)
|
||||
|
||||
Returns:
|
||||
- X: numpy array with shape [num, 224, 224, 3]
|
||||
- y: numpy array of integer image labels, shape [num]
|
||||
- class_names: dict mapping integer label to class name
|
||||
"""
|
||||
imagenet_fn = 'cs231n/datasets/imagenet_val_25.npz'
|
||||
if not os.path.isfile(imagenet_fn):
|
||||
print('file %s not found' % imagenet_fn)
|
||||
print('Run the following:')
|
||||
print('cd cs231n/datasets')
|
||||
print('bash get_imagenet_val.sh')
|
||||
assert False, 'Need to download imagenet_val_25.npz'
|
||||
f = np.load(imagenet_fn)
|
||||
X = f['X']
|
||||
y = f['y']
|
||||
class_names = f['label_map'].item()
|
||||
if num is not None:
|
||||
X = X[:num]
|
||||
y = y[:num]
|
||||
return X, y, class_names
|
||||
@ -0,0 +1,4 @@
|
||||
# Get CIFAR10
|
||||
wget http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz -O cifar-10-python.tar.gz
|
||||
tar -xzvf cifar-10-python.tar.gz
|
||||
rm cifar-10-python.tar.gz
|
||||
@ -0,0 +1,129 @@
|
||||
from __future__ import print_function
|
||||
from builtins import range
|
||||
from past.builtins import xrange
|
||||
|
||||
import numpy as np
|
||||
from random import randrange
|
||||
|
||||
def eval_numerical_gradient(f, x, verbose=True, h=0.00001):
|
||||
"""
|
||||
a naive implementation of numerical gradient of f at x
|
||||
- f should be a function that takes a single argument
|
||||
- x is the point (numpy array) to evaluate the gradient at
|
||||
"""
|
||||
|
||||
fx = f(x) # evaluate function value at original point
|
||||
grad = np.zeros_like(x)
|
||||
# iterate over all indexes in x
|
||||
it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
|
||||
while not it.finished:
|
||||
|
||||
# evaluate function at x+h
|
||||
ix = it.multi_index
|
||||
oldval = x[ix]
|
||||
x[ix] = oldval + h # increment by h
|
||||
fxph = f(x) # evalute f(x + h)
|
||||
x[ix] = oldval - h
|
||||
fxmh = f(x) # evaluate f(x - h)
|
||||
x[ix] = oldval # restore
|
||||
|
||||
# compute the partial derivative with centered formula
|
||||
grad[ix] = (fxph - fxmh) / (2 * h) # the slope
|
||||
if verbose:
|
||||
print(ix, grad[ix])
|
||||
it.iternext() # step to next dimension
|
||||
|
||||
return grad
|
||||
|
||||
|
||||
def eval_numerical_gradient_array(f, x, df, h=1e-5):
|
||||
"""
|
||||
Evaluate a numeric gradient for a function that accepts a numpy
|
||||
array and returns a numpy array.
|
||||
"""
|
||||
grad = np.zeros_like(x)
|
||||
it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
|
||||
while not it.finished:
|
||||
ix = it.multi_index
|
||||
|
||||
oldval = x[ix]
|
||||
x[ix] = oldval + h
|
||||
pos = f(x).copy()
|
||||
x[ix] = oldval - h
|
||||
neg = f(x).copy()
|
||||
x[ix] = oldval
|
||||
|
||||
grad[ix] = np.sum((pos - neg) * df) / (2 * h)
|
||||
it.iternext()
|
||||
return grad
|
||||
|
||||
|
||||
def eval_numerical_gradient_blobs(f, inputs, output, h=1e-5):
|
||||
"""
|
||||
Compute numeric gradients for a function that operates on input
|
||||
and output blobs.
|
||||
|
||||
We assume that f accepts several input blobs as arguments, followed by a
|
||||
blob where outputs will be written. For example, f might be called like:
|
||||
|
||||
f(x, w, out)
|
||||
|
||||
where x and w are input Blobs, and the result of f will be written to out.
|
||||
|
||||
Inputs:
|
||||
- f: function
|
||||
- inputs: tuple of input blobs
|
||||
- output: output blob
|
||||
- h: step size
|
||||
"""
|
||||
numeric_diffs = []
|
||||
for input_blob in inputs:
|
||||
diff = np.zeros_like(input_blob.diffs)
|
||||
it = np.nditer(input_blob.vals, flags=['multi_index'],
|
||||
op_flags=['readwrite'])
|
||||
while not it.finished:
|
||||
idx = it.multi_index
|
||||
orig = input_blob.vals[idx]
|
||||
|
||||
input_blob.vals[idx] = orig + h
|
||||
f(*(inputs + (output,)))
|
||||
pos = np.copy(output.vals)
|
||||
input_blob.vals[idx] = orig - h
|
||||
f(*(inputs + (output,)))
|
||||
neg = np.copy(output.vals)
|
||||
input_blob.vals[idx] = orig
|
||||
|
||||
diff[idx] = np.sum((pos - neg) * output.diffs) / (2.0 * h)
|
||||
|
||||
it.iternext()
|
||||
numeric_diffs.append(diff)
|
||||
return numeric_diffs
|
||||
|
||||
|
||||
def eval_numerical_gradient_net(net, inputs, output, h=1e-5):
|
||||
return eval_numerical_gradient_blobs(lambda *args: net.forward(),
|
||||
inputs, output, h=h)
|
||||
|
||||
|
||||
def grad_check_sparse(f, x, analytic_grad, num_checks=10, h=1e-5):
|
||||
"""
|
||||
sample a few random elements and only return numerical
|
||||
in this dimensions.
|
||||
"""
|
||||
|
||||
for i in range(num_checks):
|
||||
ix = tuple([randrange(m) for m in x.shape])
|
||||
|
||||
oldval = x[ix]
|
||||
x[ix] = oldval + h # increment by h
|
||||
fxph = f(x) # evaluate f(x + h)
|
||||
x[ix] = oldval - h # increment by h
|
||||
fxmh = f(x) # evaluate f(x - h)
|
||||
x[ix] = oldval # reset
|
||||
|
||||
grad_numerical = (fxph - fxmh) / (2 * h)
|
||||
grad_analytic = analytic_grad[ix]
|
||||
rel_error = (abs(grad_numerical - grad_analytic) /
|
||||
(abs(grad_numerical) + abs(grad_analytic)))
|
||||
print('numerical: %f analytic: %f, relative error: %e'
|
||||
%(grad_numerical, grad_analytic, rel_error))
|
||||
@ -0,0 +1,73 @@
|
||||
from builtins import range
|
||||
from past.builtins import xrange
|
||||
|
||||
from math import sqrt, ceil
|
||||
import numpy as np
|
||||
|
||||
def visualize_grid(Xs, ubound=255.0, padding=1):
|
||||
"""
|
||||
Reshape a 4D tensor of image data to a grid for easy visualization.
|
||||
|
||||
Inputs:
|
||||
- Xs: Data of shape (N, H, W, C)
|
||||
- ubound: Output grid will have values scaled to the range [0, ubound]
|
||||
- padding: The number of blank pixels between elements of the grid
|
||||
"""
|
||||
(N, H, W, C) = Xs.shape
|
||||
grid_size = int(ceil(sqrt(N)))
|
||||
grid_height = H * grid_size + padding * (grid_size - 1)
|
||||
grid_width = W * grid_size + padding * (grid_size - 1)
|
||||
grid = np.zeros((grid_height, grid_width, C))
|
||||
next_idx = 0
|
||||
y0, y1 = 0, H
|
||||
for y in range(grid_size):
|
||||
x0, x1 = 0, W
|
||||
for x in range(grid_size):
|
||||
if next_idx < N:
|
||||
img = Xs[next_idx]
|
||||
low, high = np.min(img), np.max(img)
|
||||
grid[y0:y1, x0:x1] = ubound * (img - low) / (high - low)
|
||||
# grid[y0:y1, x0:x1] = Xs[next_idx]
|
||||
next_idx += 1
|
||||
x0 += W + padding
|
||||
x1 += W + padding
|
||||
y0 += H + padding
|
||||
y1 += H + padding
|
||||
# grid_max = np.max(grid)
|
||||
# grid_min = np.min(grid)
|
||||
# grid = ubound * (grid - grid_min) / (grid_max - grid_min)
|
||||
return grid
|
||||
|
||||
def vis_grid(Xs):
|
||||
""" visualize a grid of images """
|
||||
(N, H, W, C) = Xs.shape
|
||||
A = int(ceil(sqrt(N)))
|
||||
G = np.ones((A*H+A, A*W+A, C), Xs.dtype)
|
||||
G *= np.min(Xs)
|
||||
n = 0
|
||||
for y in range(A):
|
||||
for x in range(A):
|
||||
if n < N:
|
||||
G[y*H+y:(y+1)*H+y, x*W+x:(x+1)*W+x, :] = Xs[n,:,:,:]
|
||||
n += 1
|
||||
# normalize to [0,1]
|
||||
maxg = G.max()
|
||||
ming = G.min()
|
||||
G = (G - ming)/(maxg-ming)
|
||||
return G
|
||||
|
||||
def vis_nn(rows):
|
||||
""" visualize array of arrays of images """
|
||||
N = len(rows)
|
||||
D = len(rows[0])
|
||||
H,W,C = rows[0][0].shape
|
||||
Xs = rows[0][0]
|
||||
G = np.ones((N*H+N, D*W+D, C), Xs.dtype)
|
||||
for y in range(N):
|
||||
for x in range(D):
|
||||
G[y*H+y:(y+1)*H+y, x*W+x:(x+1)*W+x, :] = rows[y][x]
|
||||
# normalize to [0,1]
|
||||
maxg = G.max()
|
||||
ming = G.min()
|
||||
G = (G - ming)/(maxg-ming)
|
||||
return G
|
||||
Loading…
Reference in New Issue