Convolutional Sentiment Classifier

In [1]:

    

import keras
from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding
from keras.layers import SpatialDropout1D, Conv1D, GlobalMaxPooling1D # new! 
from keras.callbacks import ModelCheckpoint
import os
from sklearn.metrics import roc_auc_score 
import matplotlib.pyplot as plt 
%matplotlib inline


    

    




Using TensorFlow backend.

In [2]:

    

# output directory name:
output_dir = 'model_output/conv'

# training:
epochs = 4
batch_size = 128

# vector-space embedding: 
n_dim = 64
n_unique_words = 5000 
max_review_length = 400
pad_type = trunc_type = 'pre'
drop_embed = 0.2 # new!

# convolutional layer architecture:
n_conv = 256 # filters, a.k.a. kernels
k_conv = 3 # kernel length

# dense layer architecture: 
n_dense = 256
dropout = 0.2


    

In [3]:

    

(x_train, y_train), (x_valid, y_valid) = imdb.load_data(num_words=n_unique_words) # removed n_words_to_skip


    

In [4]:

    

x_train = pad_sequences(x_train, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)
x_valid = pad_sequences(x_valid, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)


    

In [5]:

    

model = Sequential()
model.add(Embedding(n_unique_words, n_dim, input_length=max_review_length)) 
model.add(SpatialDropout1D(drop_embed))
model.add(Conv1D(n_conv, k_conv, activation='relu'))
# model.add(Conv1D(n_conv, k_conv, activation='relu'))
model.add(GlobalMaxPooling1D())
model.add(Dense(n_dense, activation='relu'))
model.add(Dropout(dropout))
model.add(Dense(1, activation='sigmoid'))


    

In [6]:

    

model.summary()


    

    




_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, 400, 64)           320000    
_________________________________________________________________
spatial_dropout1d_1 (Spatial (None, 400, 64)           0         
_________________________________________________________________
conv1d_1 (Conv1D)            (None, 398, 256)          49408     
_________________________________________________________________
global_max_pooling1d_1 (Glob (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 256)               65792     
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 257       
=================================================================
Total params: 435,457
Trainable params: 435,457
Non-trainable params: 0
_________________________________________________________________

In [7]:

    

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])


    

In [8]:

    

modelcheckpoint = ModelCheckpoint(filepath=output_dir+"/weights.{epoch:02d}.hdf5")
if not os.path.exists(output_dir):
    os.makedirs(output_dir)


    

In [9]:

    

# 89.1% validation accuracy in epoch 2
# ...with second convolutional layer is essentially the same at 89.0%
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_valid, y_valid), callbacks=[modelcheckpoint])


    

    




Train on 25000 samples, validate on 25000 samples
Epoch 1/4
25000/25000 [==============================] - 45s - loss: 0.4800 - acc: 0.7472 - val_loss: 0.2931 - val_acc: 0.8754
Epoch 2/4
25000/25000 [==============================] - 3s - loss: 0.2502 - acc: 0.8974 - val_loss: 0.2602 - val_acc: 0.8918
Epoch 3/4
25000/25000 [==============================] - 3s - loss: 0.1719 - acc: 0.9357 - val_loss: 0.2811 - val_acc: 0.8848
Epoch 4/4
25000/25000 [==============================] - 3s - loss: 0.1147 - acc: 0.9609 - val_loss: 0.3094 - val_acc: 0.8834






    
Out[9]:





<keras.callbacks.History at 0x7f99e7711f60>

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model.load_weights(output_dir+"/weights.01.hdf5") # zero-indexed


    

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y_hat = model.predict_proba(x_valid)


    

    




24544/25000 [============================>.] - ETA: 0s

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plt.hist(y_hat)
_ = plt.axvline(x=0.5, color='orange')


    

In [13]:

    

"{:0.2f}".format(roc_auc_score(y_valid, y_hat)*100.0)


    

    
Out[13]:





'95.97'

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