来自吴恩达深度学习系列视频：序列模型第二周作业2：Emojify!。如果英文对你来说有困难，可以参照：【中文】【吴恩达课后编程作业】Course 5 - 序列模型 - 第二周作业 - 词向量的运算与Emoji生成器，参照对英文的翻译并不能说完全准确，请注意这点。
完整的ipynb文件参见博主github：
https://github.com/Hongze-Wang/Deep-Learning-Andrew-Ng/tree/master/homework
在读取data/glove.6B.50d.txt你可能会遇到这样一个问题：
'gbk' codec can't decode byte 0x93 in position 3136
解压作业文件夹同名压缩包，并更改w2v_utils.py文件中的读取函数的with open部分如下：
Basically，增加了隐藏参数encoding指定了打开文件的编码是“utf-8”。
更改之后重启jupyter kernel即可正常运行。
Emojify!
Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.
Have you ever wanted to make your text messages more expressive? Your emojifier app will help you do that. So rather than writing “Congratulations on the promotion! Lets get coffee and talk. Love you!” the emojifier can automatically turn this into “Congratulations on the promotion! ? Lets get coffee and talk. ☕️ Love you! ❤️”
You will implement a model which inputs a sentence (such as “Let’s go see the baseball game tonight!”) and finds the most appropriate emoji to be used with this sentence (⚾️). In many emoji interfaces, you need to remember that ❤️ is the “heart” symbol rather than the “love” symbol. But using word vectors, you’ll see that even if your training set explicitly relates only a few words to a particular emoji, your algorithm will be able to generalize and associate words in the test set to the same emoji even if those words don’t even appear in the training set. This allows you to build an accurate classifier mapping from sentences to emojis, even using a small training set.
In this exercise, you’ll start with a baseline model (Emojifier-V1) using word embeddings, then build a more sophisticated model (Emojifier-V2) that further incorporates an LSTM.
Lets get started! Run the following cell to load the package you are going to use.
import numpy as np
from emo_utils import *
import emoji
import matplotlib.pyplot as plt
%matplotlib inline
1 - Baseline model: Emojifier-V1
1.1 - Dataset EMOJISET
Let’s start by building a simple baseline classifier.
You have a tiny dataset (X, Y) where:
- X contains 127 sentences (strings)
- Y contains a integer label between 0 and 4 corresponding to an emoji for each sentence
Let’s load the dataset using the code below. We split the dataset between training (127 examples) and testing (56 examples).
X_train, Y_train = read_csv('data/train_emoji.csv')
X_test, Y_test = read_csv('data/tesss.csv')
maxLen = len(max(X_train, key=len).split())
Run the following cell to print sentences from X_train and corresponding labels from Y_train. Change index to see different examples. Because of the font the iPython notebook uses, the heart emoji may be colored black rather than red.
index = 6
print(X_train[index], label_to_emoji(Y_train[index]))
Stop saying bullshit ?
1.2 - Overview of the Emojifier-V1
In this part, you are going to implement a baseline model called “Emojifier-v1”.
The input of the model is a string corresponding to a sentence (e.g. "I love you). In the code, the output will be a probability vector of shape (1,5), that you then pass in an argmax layer to extract the index of the most likely emoji output.
To get our labels into a format suitable for training a softmax classifier, lets convert
Y
Y
Y from its current shape current shape
(
m
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1
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(m, 1)
(m,1) into a “one-hot representation”
(
m
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5
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(m, 5)
(m,5), where each row is a one-hot vector giving the label of one example, You can do so using this next code snipper. Here, Y_oh stands for “Y-one-hot” in the variable names Y_oh_train and Y_oh_test:
'''
def convert_to_one_hot(Y, C):
Y = np.eye(C)[Y.reshape(-1)]
return Y
'''
Y_oh_train = convert_to_one_hot(Y_train, C = 5)
Y_oh_test = convert_to_one_hot(Y_test, C = 5)
Let’s see what convert_to_one_hot() did. Feel free to change index to print out different values.
index = 50
print(Y_train[index], "is converted into one hot", Y_oh_train[index])
0 is converted into one hot [1. 0. 0. 0. 0.]
All the data is now ready to be fed into the Emojify-V1 model. Let’s implement the model!
1.3 - Implementing Emojifier-V1
As shown in Figure (2), the first step is to convert an input sentence into the word vector representation, which then get averaged together. Similar to the previous exercise, we will use pretrained 50-dimensional GloVe embeddings. Run the following cell to load the word_to_vec_map, which contains all the vector representations.
word_to_index, index_to_word, word_to_vec_map = read_glove_vecs('data/glove.6B.50d.txt')
You’ve loaded:
word_to_index: dictionary mapping from words to their indices in the vocabulary (400,001 words, with the valid indices ranging from 0 to 400,000)index_to_word: dictionary mapping from indices to their corresponding words in the vocabularyword_to_vec_map: dictionary mapping words to their GloVe vector representation.
Run the following cell to check if it works.
word = "cucumber"
index = 289846
print("the index of", word, "in the vocabulary is", word_to_index[word])
print("the", str(index) + "th word in the vocabulary is", index_to_word[index])
the index of cucumber in the vocabulary is 113317
the 289846th word in the vocabulary is potatos
Exercise: Implement sentence_to_avg(). You will need to carry out two steps:
- Convert every sentence to lower-case, then split the sentence into a list of words.
X.lower()andX.split()might be useful. - For each word in the sentence, access its GloVe representation. Then, average all these values.
# GRADED FUNCTION: sentence_to_avg
def sentence_to_avg(sentence, word_to_vec_map):
"""
Converts a sentence (string) into a list of words (strings). Extracts the GloVe representation of each word
and averages its value into a single vector encoding the meaning of the sentence.
Arguments:
sentence -- string, one training example from X
word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation
Returns:
avg -- average vector encoding information about the sentence, numpy-array of shape (50,)
"""
### START CODE HERE ###
# Step 1: Split sentence into list of lower case words (≈ 1 line)
words = sentence.lower().split()
# Initialize the average word vector, should have the same shape as your word vectors.
avg = np.zeros(50,)
# Step 2: average the word vectors. You can loop over the words in the list "words".
for w in words:
avg += word_to_vec_map[w]
avg = np.divide(avg, len(words))
### END CODE HERE ###
return avg
avg = sentence_to_avg("Morrocan couscous is my favorite dish", word_to_vec_map)
print("avg = ", avg)
avg = [-0.008005 0.56370833 -0.50427333 0.258865 0.55131103 0.03104983
-0.21013718 0.16893933 -0.09590267 0.141784 -0.15708967 0.18525867
0.6495785 0.38371117 0.21102167 0.11301667 0.02613967 0.26037767
0.05820667 -0.01578167 -0.12078833 -0.02471267 0.4128455 0.5152061
0.38756167 -0.898661 -0.535145 0.33501167 0.68806933 -0.2156265
1.797155 0.10476933 -0.36775333 0.750785 0.10282583 0.348925
-0.27262833 0.66768 -0.10706167 -0.283635 0.59580117 0.28747333
-0.3366635 0.23393817 0.34349183 0.178405 0.1166155 -0.076433
0.1445417 0.09808667]
Model
You now have all the pieces to finish implementing the model() function. After using sentence_to_avg() you need to pass the average through forward propagation, compute the cost, and then backpropagate to update the softmax’s parameters.
Exercise: Implement the model() function described in Figure (2). Assuming here that
Y
o
h
Yoh
Yoh (“Y one hot”) is the one-hot encoding of the output labels, the equations you need to implement in the forward pass and to compute the cross-entropy cost are:
z
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=
W
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z^{(i)} = W . avg^{(i)} + b
z(i)=W.avg(i)+b
a
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=
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a^{(i)} = softmax(z^{(i)})
a(i)=softmax(z(i))
L
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mathcal{L}^{(i)} = - sum_{k = 0}^{n_y - 1} Yoh^{(i)}_k * log(a^{(i)}_k)
L(i)=−k=0∑ny−1Yohk(i)∗log(ak(i))
It is possible to come up with a more efficient vectorized implementation. But since we are using a for-loop to convert the sentences one at a time into the avg^{(i)} representation anyway, let’s not bother this time.
We provided you a function softmax().
# GRADED FUNCTION: model
def model(X, Y, word_to_vec_map, learning_rate = 0.01, num_iterations = 400):
"""
Model to train word vector representations in numpy.
Arguments:
X -- input data, numpy array of sentences as strings, of shape (m, 1)
Y -- labels, numpy array of integers between 0 and 7, numpy-array of shape (m, 1)
word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation
learning_rate -- learning_rate for the stochastic gradient descent algorithm
num_iterations -- number of iterations
Returns:
pred -- vector of predictions, numpy-array of shape (m, 1)
W -- weight matrix of the softmax layer, of shape (n_y, n_h)
b -- bias of the softmax layer, of shape (n_y,)
"""
np.random.seed(1)
# Define number of training examples
m = Y.shape[0] # number of training examples
n_y = 5 # number of classes
n_h = 50 # dimensions of the GloVe vectors
# Initialize parameters using Xavier initialization
W = np.random.randn(n_y, n_h) / np.sqrt(n_h)
b = np.zeros((n_y,))
# Convert Y to Y_onehot with n_y classes
Y_oh = convert_to_one_hot(Y, C = n_y)
# Optimization loop
for t in range(num_iterations): # Loop over the number of iterations
for i in range(m): # Loop over the training examples
### START CODE HERE ### (≈ 4 lines of code)
# Average the word vectors of the words from the i'th training example
avg = sentence_to_avg(X[i], word_to_vec_map)
# Forward propagate the avg through the softmax layer
z = np.dot(W, avg) + b
a = softmax(z)
# Compute cost using the i'th training label's one hot representation and "A" (the output of the softmax)
#cost = -np.sum(np.dot(Y_oh[1], np.log(a)))
cost = -np.sum(Y_oh[1] * np.log(a))
### END CODE HERE ###
# backprop Steve add this note
# Compute gradients
dz = a - Y_oh[i]
dW = np.dot(dz.reshape(n_y,1), avg.reshape(1, n_h))
db = dz
# Update parameters with Stochastic Gradient Descent
W = W - learning_rate * dW
b = b - learning_rate * db
if t % 100 == 0:
print("Epoch: " + str(t) + " --- cost = " + str(cost))
pred = predict(X, Y, W, b, word_to_vec_map)
return pred, W, b
print(X_train.shape)
print(Y_train.shape)
print(np.eye(5)[Y_train.reshape(-1)].shape)
print(X_train[0])
print(type(X_train))
Y = np.asarray([5,0,0,5, 4, 4, 4, 6, 6, 4, 1, 1, 5, 6, 6, 3, 6, 3, 4, 4])
print(Y.shape)
X = np.asarray(['I am going to the bar tonight', 'I love you', 'miss you my dear',
'Lets go party and drinks','Congrats on the new job','Congratulations',
'I am so happy for you', 'Why are you feeling bad', 'What is wrong with you',
'You totally deserve this prize', 'Let us go play football',
'Are you down for football this afternoon', 'Work hard play harder',
'It is suprising how people can be dumb sometimes',
'I am very disappointed','It is the best day in my life',
'I think I will end up alone','My life is so boring','Good job',
'Great so awesome'])
print(X.shape)
print(np.eye(5)[Y_train.reshape(-1)].shape)
print(type(X_train))
(132,)
(132,)
(132, 5)
never talk to me again
<class 'numpy.ndarray'>
(20,)
(20,)
(132, 5)
<class 'numpy.ndarray'>
Run the next cell to train your model and learn the softmax parameters (W,b).
pred, W, b = model(X_train, Y_train, word_to_vec_map)
print(pred)
Epoch: 0 --- cost = 1.9520498812810072
Accuracy: 0.3484848484848485
Epoch: 100 --- cost = 0.07971818726014807
Accuracy: 0.9318181818181818
Epoch: 200 --- cost = 0.04456369243681402
Accuracy: 0.9545454545454546
Epoch: 300 --- cost = 0.03432267378786059
Accuracy: 0.9696969696969697
[[3.]
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[0.]
[2.]]
Great! Your model has pretty high accuracy on the training set. Lets now see how it does on the test set.
1.4 - Examining test set performance
print("Training set:")
pred_train = predict(X_train, Y_train, W, b, word_to_vec_map)
print('Test set:')
pred_test = predict(X_test, Y_test, W, b, word_to_vec_map)
Training set:
Accuracy: 0.9772727272727273
Test set:
Accuracy: 0.8571428571428571
这里的准确度应该和我完全一致才是对的，因为你是手动实现的整个过程，没有调用Keras，v2版本的准确度就不是这样了，具体见下。
Random guessing would have had 20% accuracy given that there are 5 classes. This is pretty good performance after training on only 127 examples.
In the training set, the algorithm saw the sentence “I love you” with the label ❤️. You can check however that the word “adore” does not appear in the training set. Nonetheless, lets see what happens if you write “I adore you.”
X_my_sentences = np.array(["i adore you", "i love you", "funny lol", "lets play with a ball", "food is ready", "not feeling happy"])
Y_my_labels = np.array([[0], [0], [2], [1], [4],[3]])
pred = predict(X_my_sentences, Y_my_labels , W, b, word_to_vec_map)
print_predictions(X_my_sentences, pred)
Accuracy: 0.8333333333333334
i adore you ❤️
i love you ❤️
funny lol ?
lets play with a ball ⚾
food is ready ?
not feeling happy ?
Amazing! Because adore has a similar embedding as love, the algorithm has generalized correctly even to a word it has never seen before. Words such as heart, dear, beloved or adore have embedding vectors similar to love, and so might work too—feel free to modify the inputs above and try out a variety of input sentences. How well does it work?
Note though that it doesn’t get “not feeling happy” correct. This algorithm ignores word ordering, so is not good at understanding phrases like “not happy.”
Printing the confusion matrix can also help understand which classes are more difficult for your model. A confusion matrix shows how often an example whose label is one class (“actual” class) is mislabeled by the algorithm with a different class (“predicted” class).
print(Y_test.shape)
print(' '+ label_to_emoji(0)+ ' ' + label_to_emoji(1) + ' ' + label_to_emoji(2)+ ' ' + label_to_emoji(3)+' ' + label_to_emoji(4))
print(pd.crosstab(Y_test, pred_test.reshape(56,), rownames=['Actual'], colnames=['Predicted'], margins=True))
plot_confusion_matrix(Y_test, pred_test)
(56,)
❤️ ⚾ ? ? ?
Predicted 0.0 1.0 2.0 3.0 4.0 All
Actual
0 6 0 0 1 0 7
1 0 8 0 0 0 8
2 2 0 16 0 0 18
3 1 1 2 12 0 16
4 0 0 1 0 6 7
All 9 9 19 13 6 56
What you should remember from this part:
- Even with a 127 training examples, you can get a reasonably good model for Emojifying. This is due to the generalization power word vectors gives you.
- Emojify-V1 will perform poorly on sentences such as “This movie is not good and not enjoyable” because it doesn’t understand combinations of words–it just averages all the words’ embedding vectors together, without paying attention to the ordering of words. You will build a better algorithm in the next part.
2 - Emojifier-V2: Using LSTMs in Keras:
Let’s build an LSTM model that takes as input word sequences. This model will be able to take word ordering into account. Emojifier-V2 will continue to use pre-trained word embeddings to represent words, but will feed them into an LSTM, whose job it is to predict the most appropriate emoji.
Run the following cell to load the Keras packages.
import numpy as np
np.random.seed(0)
from keras.models import Model
from keras.layers import Dense, Input, Dropout, LSTM, Activation
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.initializers import glorot_uniform
np.random.seed(1)
Using TensorFlow backend.
2.1 - Overview of the model
Here is the Emojifier-v2 you will implement:
2.2 Keras and mini-batching
In this exercise, we want to train Keras using mini-batches. However, most deep learning frameworks require that all sequences in the same mini-batch have the same length. This is what allows vectorization to work: If you had a 3-word sentence and a 4-word sentence, then the computations needed for them are different (one takes 3 steps of an LSTM, one takes 4 steps) so it’s just not possible to do them both at the same time.
The common solution to this is to use padding. Specifically, set a maximum sequence length, and pad all sequences to the same length. For example, of the maximum sequence length is 20, we could pad every sentence with "0"s so that each input sentence is of length 20. Thus, a sentence “i love you” would be represented as ( e i , e l o v e , e y o u , 0 ⃗ , 0 ⃗ , … , 0 ⃗ ) (e_{i}, e_{love}, e_{you}, vec{0}, vec{0}, ldots, vec{0}) (ei,elove,eyou,0,0,…,0). In this example, any sentences longer than 20 words would have to be truncated. One simple way to choose the maximum sequence length is to just pick the length of the longest sentence in the training set.
2.3 - The Embedding layer
In Keras, the embedding matrix is represented as a “layer”, and maps positive integers (indices corresponding to words) into dense vectors of fixed size (the embedding vectors). It can be trained or initialized with a pretrained embedding. In this part, you will learn how to create an Embedding() layer in Keras, initialize it with the GloVe 50-dimensional vectors loaded earlier in the notebook. Because our training set is quite small, we will not update the word embeddings but will instead leave their values fixed. But in the code below, we’ll show you how Keras allows you to either train or leave fixed this layer.
The Embedding() layer takes an integer matrix of size (batch size, max input length) as input. This corresponds to sentences converted into lists of indices (integers), as shown in the figure below.
The largest integer (i.e. word index) in the input should be no larger than the vocabulary size. The layer outputs an array of shape (batch size, max input length, dimension of word vectors).
The first step is to convert all your training sentences into lists of indices, and then zero-pad all these lists so that their length is the length of the longest sentence.
Exercise: Implement the function below to convert X (array of sentences as strings) into an array of indices corresponding to words in the sentences. The output shape should be such that it can be given to Embedding() (described in Figure 4).
# GRADED FUNCTION: sentences_to_indices
def sentences_to_indices(X, word_to_index, max_len):
"""
Converts an array of sentences (strings) into an array of indices corresponding to words in the sentences.
The output shape should be such that it can be given to `Embedding()` (described in Figure 4).
Arguments:
X -- array of sentences (strings), of shape (m, 1)
word_to_index -- a dictionary containing the each word mapped to its index
max_len -- maximum number of words in a sentence. You can assume every sentence in X is no longer than this.
Returns:
X_indices -- array of indices corresponding to words in the sentences from X, of shape (m, max_len)
"""
m = X.shape[0] # number of training examples
### START CODE HERE ###
# Initialize X_indices as a numpy matrix of zeros and the correct shape (≈ 1 line)
X_indices = np.zeros((m, max_len))
for i in range(m): # loop over training examples
# Convert the ith training sentence in lower case and split is into words. You should get a list of words.
sentence_words = X[i].lower().split()
# Initialize j to 0
j = 0
# Loop over the words of sentence_words
for w in sentence_words:
# Set the (i,j)th entry of X_indices to the index of the correct word.
X_indices[i, j] = word_to_index[w]
# Increment j to j + 1
j = j+1
### END CODE HERE ###
return X_indices
Run the following cell to check what sentences_to_indices() does, and check your results.
X1 = np.array(["funny lol", "lets play baseball", "food is ready for you"])
X1_indices = sentences_to_indices(X1,word_to_index, max_len = 5)
print("X1 =", X1)
print("X1_indices =", X1_indices)
X1 = ['funny lol' 'lets play baseball' 'food is ready for you']
X1_indices = [[155345. 225122. 0. 0. 0.]
[220930. 286375. 69714. 0. 0.]
[151204. 192973. 302254. 151349. 394475.]]
Let’s build the Embedding() layer in Keras, using pre-trained word vectors. After this layer is built, you will pass the output of sentences_to_indices() to it as an input, and the Embedding() layer will return the word embeddings for a sentence.
Exercise: Implement pretrained_embedding_layer(). You will need to carry out the following steps:
- Initialize the embedding matrix as a numpy array of zeroes with the correct shape.
- Fill in the embedding matrix with all the word embeddings extracted from
word_to_vec_map. - Define Keras embedding layer. Use Embedding(). Be sure to make this layer non-trainable, by setting
trainable = Falsewhen callingEmbedding(). If you were to settrainable = True, then it will allow the optimization algorithm to modify the values of the word embeddings. - Set the embedding weights to be equal to the embedding matrix
# GRADED FUNCTION: pretrained_embedding_layer
def pretrained_embedding_layer(word_to_vec_map, word_to_index):
"""
Creates a Keras Embedding() layer and loads in pre-trained GloVe 50-dimensional vectors.
Arguments:
word_to_vec_map -- dictionary mapping words to their GloVe vector representation.
word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)
Returns:
embedding_layer -- pretrained layer Keras instance
"""
vocab_len = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)
emb_dim = word_to_vec_map["cucumber"].shape[0] # define dimensionality of your GloVe word vectors (= 50)
### START CODE HERE ###
# Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)
emb_matrix = np.zeros((vocab_len, emb_dim))
# Set each row "index" of the embedding matrix to be the word vector representation of the "index"th word of the vocabulary
for word, index in word_to_index.items():
emb_matrix[index, :] = word_to_vec_map[word]
# Define Keras embedding layer with the correct output/input sizes, make it untrainable. Use Embedding(...). Make sure to set trainable=False.
embedding_layer = Embedding(vocab_len, emb_dim, trainable=False)
### END CODE HERE ###
# Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the "None".
embedding_layer.build((None,))
# Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.
embedding_layer.set_weights([emb_matrix])
return embedding_layer
embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)
print("weights[0][1][3] =", embedding_layer.get_weights()[0][1][3])
weights[0][1][3] = -0.3403
2.3 Building the Emojifier-V2
Lets now build the Emojifier-V2 model. You will do so using the embedding layer you have built, and feed its output to an LSTM network.
Exercise: Implement Emojify_V2(), which builds a Keras graph of the architecture shown in Figure 3. The model takes as input an array of sentences of shape (m, max_len, ) defined by input_shape. It should output a softmax probability vector of shape (m, C = 5). You may need Input(shape = ..., dtype = '...'), LSTM(), Dropout(), Dense(), and Activation().
# GRADED FUNCTION: Emojify_V2
def Emojify_V2(input_shape, word_to_vec_map, word_to_index):
"""
Function creating the Emojify-v2 model's graph.
Arguments:
input_shape -- shape of the input, usually (max_len,)
word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation
word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)
Returns:
model -- a model instance in Keras
"""
### START CODE HERE ###
# Define sentence_indices as the input of the graph, it should be of shape input_shape and dtype 'int32' (as it contains indices).
sentence_indices = Input(input_shape, dtype='int32')
# Create the embedding layer pretrained with GloVe Vectors (≈1 line)
embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)
# Propagate sentence_indices through your embedding layer, you get back the embeddings
embeddings = embedding_layer(sentence_indices)
# Propagate the embeddings through an LSTM layer with 128-dimensional hidden state
# Be careful, the returned output should be a batch of sequences.
X = LSTM(128, return_sequences=True)(embeddings)
# Add dropout with a probability of 0.5
X = Dropout(0.5)(X)
# Propagate X trough another LSTM layer with 128-dimensional hidden state
# Be careful, the returned output should be a single hidden state, not a batch of sequences.
X = LSTM(128, return_sequences=False)(X)
# Add dropout with a probability of 0.5
X = Dropout(0.5)(X)
# Propagate X through a Dense layer with softmax activation to get back a batch of 5-dimensional vectors.
X = Dense(5)(X)
# Add a softmax activation
X = Activation('softmax')(X)
# Create Model instance which converts sentence_indices into X.
model = Model(inputs=sentence_indices, outputs=X)
### END CODE HERE ###
return model
Run the following cell to create your model and check its summary. Because all sentences in the dataset are less than 10 words, we chose max_len = 10. You should see your architecture, it uses “20,223,927” parameters, of which 20,000,050 (the word embeddings) are non-trainable, and the remaining 223,877 are. Because our vocabulary size has 400,001 words (with valid indices from 0 to 400,000) there are 400,001*50 = 20,000,050 non-trainable parameters.
model = Emojify_V2((maxLen,), word_to_vec_map, word_to_index)
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 10) 0
_________________________________________________________________
embedding_2 (Embedding) (None, 10, 50) 20000050
_________________________________________________________________
lstm_1 (LSTM) (None, 10, 128) 91648
_________________________________________________________________
dropout_1 (Dropout) (None, 10, 128) 0
_________________________________________________________________
lstm_2 (LSTM) (None, 128) 131584
_________________________________________________________________
dropout_2 (Dropout) (None, 128) 0
_________________________________________________________________
dense_1 (Dense) (None, 5) 645
_________________________________________________________________
activation_1 (Activation) (None, 5) 0
=================================================================
Total params: 20,223,927
Trainable params: 223,877
Non-trainable params: 20,000,050
_________________________________________________________________
As usual, after creating your model in Keras, you need to compile it and define what loss, optimizer and metrics your are want to use. Compile your model using categorical_crossentropy loss, adam optimizer and ['accuracy'] metrics:
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
It’s time to train your model. Your Emojifier-V2 model takes as input an array of shape (m, max_len) and outputs probability vectors of shape (m, number of classes). We thus have to convert X_train (array of sentences as strings) to X_train_indices (array of sentences as list of word indices), and Y_train (labels as indices) to Y_train_oh (labels as one-hot vectors).
X_train_indices = sentences_to_indices(X_train, word_to_index, maxLen)
Y_train_oh = convert_to_one_hot(Y_train, C = 5)
Fit the Keras model on X_train_indices and Y_train_oh. We will use epochs = 50 and batch_size = 32.
model.fit(X_train_indices, Y_train_oh, epochs = 50, batch_size = 32, shuffle=True)
Epoch 1/50
132/132 [==============================] - 1s 8ms/step - loss: 1.6084 - acc: 0.1742
Epoch 2/50
132/132 [==============================] - 0s 433us/step - loss: 1.5344 - acc: 0.3106
Epoch 3/50
132/132 [==============================] - 0s 395us/step - loss: 1.5031 - acc: 0.3030
Epoch 4/50
132/132 [==============================] - 0s 418us/step - loss: 1.4414 - acc: 0.3561
Epoch 5/50
132/132 [==============================] - 0s 448us/step - loss: 1.3519 - acc: 0.4394
Epoch 6/50
132/132 [==============================] - 0s 410us/step - loss: 1.2381 - acc: 0.5303
Epoch 7/50
132/132 [==============================] - 0s 433us/step - loss: 1.1782 - acc: 0.4621
Epoch 8/50
132/132 [==============================] - 0s 403us/step - loss: 1.0561 - acc: 0.5682
Epoch 9/50
132/132 [==============================] - 0s 501us/step - loss: 0.8776 - acc: 0.7045
Epoch 10/50
132/132 [==============================] - 0s 486us/step - loss: 0.8231 - acc: 0.7045
Epoch 11/50
132/132 [==============================] - 0s 494us/step - loss: 0.7027 - acc: 0.7424
Epoch 12/50
132/132 [==============================] - 0s 448us/step - loss: 0.5987 - acc: 0.7955
Epoch 13/50
132/132 [==============================] - 0s 486us/step - loss: 0.4902 - acc: 0.8258
Epoch 14/50
132/132 [==============================] - 0s 509us/step - loss: 0.5116 - acc: 0.8333
Epoch 15/50
132/132 [==============================] - 0s 486us/step - loss: 0.4821 - acc: 0.8182
Epoch 16/50
132/132 [==============================] - 0s 463us/step - loss: 0.3522 - acc: 0.8636
Epoch 17/50
132/132 [==============================] - 0s 425us/step - loss: 0.3907 - acc: 0.8561
Epoch 18/50
132/132 [==============================] - 0s 410us/step - loss: 0.6514 - acc: 0.8182
Epoch 19/50
132/132 [==============================] - 0s 418us/step - loss: 0.5185 - acc: 0.8106
Epoch 20/50
132/132 [==============================] - 0s 395us/step - loss: 0.3957 - acc: 0.8409
Epoch 21/50
132/132 [==============================] - 0s 410us/step - loss: 0.4656 - acc: 0.8106
Epoch 22/50
132/132 [==============================] - 0s 418us/step - loss: 0.3905 - acc: 0.8636
Epoch 23/50
132/132 [==============================] - 0s 425us/step - loss: 0.3720 - acc: 0.8561
Epoch 24/50
132/132 [==============================] - 0s 418us/step - loss: 0.3063 - acc: 0.9091
Epoch 25/50
132/132 [==============================] - 0s 433us/step - loss: 0.3419 - acc: 0.8864
Epoch 26/50
132/132 [==============================] - 0s 425us/step - loss: 0.2463 - acc: 0.9394
Epoch 27/50
132/132 [==============================] - 0s 471us/step - loss: 0.3125 - acc: 0.8788
Epoch 28/50
132/132 [==============================] - 0s 441us/step - loss: 0.2446 - acc: 0.9318
Epoch 29/50
132/132 [==============================] - 0s 425us/step - loss: 0.3845 - acc: 0.8712
Epoch 30/50
132/132 [==============================] - 0s 410us/step - loss: 0.2603 - acc: 0.9091
Epoch 31/50
132/132 [==============================] - 0s 433us/step - loss: 0.2918 - acc: 0.8864
Epoch 32/50
132/132 [==============================] - 0s 448us/step - loss: 0.1944 - acc: 0.9318
Epoch 33/50
132/132 [==============================] - 0s 441us/step - loss: 0.2067 - acc: 0.9470
Epoch 34/50
132/132 [==============================] - 0s 471us/step - loss: 0.1607 - acc: 0.9621
Epoch 35/50
132/132 [==============================] - 0s 448us/step - loss: 0.1657 - acc: 0.9621
Epoch 36/50
132/132 [==============================] - 0s 433us/step - loss: 0.1938 - acc: 0.9394
Epoch 37/50
132/132 [==============================] - 0s 441us/step - loss: 0.2172 - acc: 0.9318
Epoch 38/50
132/132 [==============================] - 0s 471us/step - loss: 0.2419 - acc: 0.9318
Epoch 39/50
132/132 [==============================] - 0s 456us/step - loss: 0.1482 - acc: 0.9470
Epoch 40/50
132/132 [==============================] - 0s 433us/step - loss: 0.1772 - acc: 0.9394
Epoch 41/50
132/132 [==============================] - 0s 471us/step - loss: 0.0890 - acc: 0.9848
Epoch 42/50
132/132 [==============================] - 0s 448us/step - loss: 0.0959 - acc: 0.9621
Epoch 43/50
132/132 [==============================] - 0s 395us/step - loss: 0.0877 - acc: 0.9621
Epoch 44/50
132/132 [==============================] - 0s 456us/step - loss: 0.0537 - acc: 0.9924
Epoch 45/50
132/132 [==============================] - 0s 433us/step - loss: 0.0733 - acc: 0.9848
Epoch 46/50
132/132 [==============================] - 0s 479us/step - loss: 0.0735 - acc: 0.9773
Epoch 47/50
132/132 [==============================] - 0s 494us/step - loss: 0.0938 - acc: 0.9621
Epoch 48/50
132/132 [==============================] - 0s 456us/step - loss: 0.2296 - acc: 0.9318
Epoch 49/50
132/132 [==============================] - 0s 418us/step - loss: 0.0787 - acc: 0.9773
Epoch 50/50
132/132 [==============================] - 0s 448us/step - loss: 0.0581 - acc: 0.9924
<keras.callbacks.History at 0x244051a54e0>
这里你的准确度和我不一致也没有什么关系，多训练几次你甚至能达到1.但准确度不应该低于90%。
Your model should perform close to 100% accuracy on the training set. The exact accuracy you get may be a little different. Run the following cell to evaluate your model on the test set.
X_test_indices = sentences_to_indices(X_test, word_to_index, max_len = maxLen)
Y_test_oh = convert_to_one_hot(Y_test, C = 5)
loss, acc = model.evaluate(X_test_indices, Y_test_oh)
print()
print("Test accuracy = ", acc)
56/56 [==============================] - 0s 143us/step
Test accuracy = 0.8571428656578064
这里的准确度不应低于80%，多训练几次，如果你代码没错的话，测试集准确度可以高过90%。
You should get a test accuracy between 80% and 95%. Run the cell below to see the mislabelled examples.
# This code allows you to see the mislabelled examples
C = 5
y_test_oh = np.eye(C)[Y_test.reshape(-1)]
X_test_indices = sentences_to_indices(X_test, word_to_index, maxLen)
pred = model.predict(X_test_indices)
for i in range(len(X_test)):
x = X_test_indices
num = np.argmax(pred[i])
if(num != Y_test[i]):
print('Expected emoji:'+ label_to_emoji(Y_test[i]) + ' prediction: '+ X_test[i] + label_to_emoji(num).strip())
Expected emoji:? prediction: she got me a nice present ❤️
Expected emoji:? prediction: work is hard ?
Expected emoji:? prediction: This girl is messing with me ❤️
Expected emoji:? prediction: any suggestions for dinner ?
Expected emoji:❤️ prediction: I love taking breaks ?
Expected emoji:? prediction: you brighten my day ❤️
Expected emoji:? prediction: will you be my valentine ❤️
Expected emoji:? prediction: go away ⚾
Now you can try it on your own example. Write your own sentence below.
# Change the sentence below to see your prediction. Make sure all the words are in the Glove embeddings.
x_test = np.array(['not feeling happy'])
X_test_indices = sentences_to_indices(x_test, word_to_index, maxLen)
print(x_test[0] +' '+ label_to_emoji(np.argmax(model.predict(X_test_indices))))
not feeling happy ?
这里要是输出了一个笑脸，你就再去训练几次吧，哈哈哈哈哈。
Previously, Emojify-V1 model did not correctly label “not feeling happy,” but our implementation of Emojiy-V2 got it right. (Keras’ outputs are slightly random each time, so you may not have obtained the same result.) The current model still isn’t very robust at understanding negation (like “not happy”) because the training set is small and so doesn’t have a lot of examples of negation. But if the training set were larger, the LSTM model would be much better than the Emojify-V1 model at understanding such complex sentences.
Congratulations!
You have completed this notebook! ❤️❤️❤️
**What you should remember**: - If you have an NLP task where the training set is small, using word embeddings can help your algorithm significantly. Word embeddings allow your model to work on words in the test set that may not even have appeared in your training set. - Training sequence models in Keras (and in most other deep learning frameworks) requires a few important details: - To use mini-batches, the sequences need to be padded so that all the examples in a mini-batch have the same length. - An `Embedding()` layer can be initialized with pretrained values. These values can be either fixed or trained further on your dataset. If however your labeled dataset is small, it's usually not worth trying to train a large pre-trained set of embeddings. - `LSTM()` has a flag called `return_sequences` to decide if you would like to return every hidden states or only the last one. - You can use `Dropout()` right after `LSTM()` to regularize your network.Congratulations on finishing this assignment and building an Emojifier. We hope you’re happy with what you’ve accomplished in this notebook!
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Acknowledgments
Thanks to Alison Darcy and the Woebot team for their advice on the creation of this assignment. Woebot is a chatbot friend that is ready to speak with you 24/7. As part of Woebot’s technology, it uses word embeddings to understand the emotions of what you say. You can play with it by going to http://woebot.io
