File size: 2,058 Bytes
b20e175 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import csv
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def read_glove_vecs(glove_file):
with open(glove_file, 'r',encoding='utf-8') as f:
words = set()
word_to_vec_map = {}
for line in f:
line = line.strip().split()
curr_word = line[0]
words.add(curr_word)
word_to_vec_map[curr_word] = np.array(line[1:], dtype=np.float64)
i = 4
words_to_index = {}
index_to_words = {}
for w in sorted(words):
words_to_index[w] = i
index_to_words[i] = w
i = i + 1
return words_to_index, index_to_words, word_to_vec_map
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum()
def convert_to_one_hot(Y, C):
Y = np.eye(C)[Y.reshape(-1)]
return Y
def predict(X, Y, W, b, word_to_vec_map):
"""
Given X (sentences) and Y (emoji indices), predict emojis and compute the accuracy of your model over the given set.
Arguments:
X -- input data containing sentences, numpy array of shape (m, None)
Y -- labels, containing index of the label emoji, numpy array of shape (m, 1)
Returns:
pred -- numpy array of shape (m, 1) with your predictions
"""
m = X.shape[0]
pred = np.zeros((m, 1))
for j in range(m): # Loop over training examples
# Split jth test example (sentence) into list of lower case words
words = X[j].lower().split()
# Average words' vectors
avg = np.zeros((50,))
for w in words:
avg += word_to_vec_map[w]
avg = avg/len(words)
# Forward propagation
Z = np.dot(W, avg) + b
A = softmax(Z)
pred[j] = np.argmax(A)
print("Accuracy: " + str(np.mean((pred[:] == Y.reshape(Y.shape[0],1)[:]))))
return pred
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