Adding back examples

Author: lukas

examples/amazon-reviews/amazon-bow.py

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+from keras.preprocessing import sequence
+from keras.preprocessing import text
+import amazon
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation
+from keras.layers import Embedding, LSTM
+from keras.layers import Conv1D, Flatten
+from keras.preprocessing import text
+import wandb
+from wandb.keras import WandbCallback
+
+wandb.init()
+config = wandb.config
+config.vocab_size = 1000
+
+(train_summary, train_review_text, train_labels), (test_summary, test_review_text, test_labels) = amazon.load_amazon()
+
+config.vocab_size = 1000
+config.maxlen = 1000
+config.batch_size = 32
+config.embedding_dims = 50
+config.filters = 250
+config.kernel_size = 3
+config.hidden_dims = 250
+config.epochs = 10
+
+(X_train, y_train), (X_test, y_test) = (train_summary, train_labels), (test_summary, test_labels)
+print("Review", X_train[0])
+print("Label", y_train[0])
+
+tokenizer = text.Tokenizer(num_words=config.vocab_size)
+tokenizer.fit_on_texts(X_train)
+X_train = tokenizer.texts_to_sequences(X_train)
+X_test = tokenizer.texts_to_sequences(X_test)
+
+X_train = sequence.pad_sequences(X_train, maxlen=config.maxlen)
+X_test = sequence.pad_sequences(X_test, maxlen=config.maxlen)
+print(X_train.shape)
+print("After pre-processing", X_train[0])
+
+model = Sequential()
+model.add(Embedding(config.vocab_size,
+                    config.embedding_dims,
+                    input_length=config.maxlen))
+model.add(Dropout(0.5))
+model.add(Conv1D(config.filters,
+                 config.kernel_size,
+                 padding='valid',
+                 activation='relu'))
+model.add(Flatten())
+model.add(Dense(config.hidden_dims, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(1, activation='sigmoid'))
+
+
+model.compile(loss='binary_crossentropy',
+              optimizer='adam',
+              metrics=['accuracy'])
+model.fit(X_train, train_labels, batch_size=100, epochs=10, validation_data=(X_test, test_labels),
+    callbacks=[WandbCallback()])

examples/amazon-reviews/amazon.py

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+import os
+import json
+
+def load_amazon():
+    filename = 'reviews_Video_Games_5.json'
+    train_summary = []
+    train_review_text = []
+    train_labels = []
+    
+    test_summary = []
+    test_review_text = []
+    test_labels = []
+    
+    with open(filename, 'r') as f:
+        for (i, line) in enumerate(f):
+            data = json.loads(line)
+            
+            if data['overall'] == 3:
+                next
+            elif data['overall'] == 4 or data['overall'] == 5:
+                label = 1
+            elif data['overall'] == 1 or data['overall'] == 2:
+                label = 0
+            else:
+                raise Exception("Unexpected value " + str(data['overall']))
+                
+            summary = data['summary']
+            review_text = data['reviewText']
+            
+            if (i % 10 == 0):
+                test_summary.append(summary)
+                test_review_text.append(review_text)
+                test_labels.append(label)
+            else:
+                train_summary.append(summary)
+                train_review_text.append(review_text)
+                train_labels.append(label)
+                
+    return (train_summary, train_review_text, train_labels), (test_summary, test_review_text, test_labels)
+
+load_amazon()

examples/amazon-reviews/download-amazon.sh

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+wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Video_Games_5.json.gz 
+gunzip reviews_Video_Games_5.json.gz