lstm_opcode.py

# coding: utf-8

# # Bidirectional LSTM in Keras

# In this notebook, we use a *bidirectional* LSTM 

# #### Load dependencies

# 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, SpatialDropout1D, LSTM
from keras.layers.wrappers import Bidirectional # new! 
from keras.callbacks import ModelCheckpoint
import os
from sklearn.metrics import roc_auc_score 
import matplotlib.pyplot as plt 
get_ipython().run_line_magic('matplotlib', 'inline')


# #### Set hyperparameters

# In[2]:


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

# training:
epochs = 6
batch_size = 128

# vector-space embedding: 
n_dim = 64 
n_unique_words = 10000 
max_review_length = 200 # doubled!
pad_type = trunc_type = 'pre'
drop_embed = 0.2 

# LSTM layer architecture:
n_lstm = 256 
drop_lstm = 0.2


# #### Load data

# In[3]:


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


# #### Preprocess data

# 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)


# #### Design neural network architecture

# In[5]:


model = Sequential()
model.add(Embedding(n_unique_words, n_dim, input_length=max_review_length)) 
model.add(SpatialDropout1D(drop_embed))
model.add(Bidirectional(LSTM(n_lstm, dropout=drop_lstm)))
model.add(Dense(1, activation='sigmoid'))


# In[6]:


# LSTM layer parameters double due to both reading directions
model.summary() 


# #### Configure model

# 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)


# #### Train!

# In[9]:


# - we see 87.0% validation accuracy in epoch 2
# - with this toy dataset, the complex interplay of words over long sentence segments, won't be learned much
# - so our CNN picking up location-invariant segments of two to four words that predict review sentiment
# - these are simpler and so easier to learn from the data
# - CNN therefore outperforms on the IMDB data set
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_valid, y_valid), callbacks=[modelcheckpoint])


# #### Evaluate

# In[10]:


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


# In[11]:


y_hat = model.predict_proba(x_valid)


# In[12]:


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)