In [ ]:
# How to setup a Simple RNN model for imdb sentiment analysis in Keras

def Snippet_382(): 

    print()
    print(format('How to setup a Simple RNN model for imdb sentiment analysis in Keras','*^92'))

    import time
    start_time = time.time()

    # load libraries
    from keras.datasets import imdb
    from keras.models import Sequential
    from keras.layers import Dense, Flatten
    from keras.layers.embeddings import Embedding
    from keras.layers.recurrent import SimpleRNN
    from keras.preprocessing import sequence
    
    # load data and Set the number of words we want
    top_words = 5000; input_length = 500; #max_words

    # Load data and target vector from movie review data
    (X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
    
    print(); print(X_train.shape); print(X_train)
    print(); print(y_train.shape); print(y_train)    
    print(); print(X_test.shape);  print(X_test)
    print(); print(y_test.shape);  print(y_test)    
    
    # Convert movie review data to feature matrix
    X_train = sequence.pad_sequences(X_train, maxlen=input_length)
    print(); print(X_train.shape); print(X_train)

    X_test = sequence.pad_sequences(X_test, maxlen=input_length)
    print(); print(X_test.shape);  print(X_test)

    # setup a Simple RNN network
    model = Sequential()
    model.add(Embedding(top_words, 32, input_length=input_length))

    model.add(SimpleRNN(512, return_sequences=False, input_shape=(input_length, 32),
                        unroll=True))

    model.add(Dense(500, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

    model.summary()

    # Fit the model
    model.fit(X_train, y_train, validation_data=(X_test, y_test), 
              epochs=20, batch_size=128, verbose=1)

    # Final evaluation of the model
    scores = model.evaluate(X_test, y_test, verbose=1)
    print("Accuracy: %.2f%%" % (scores[1]*100))
    print(); print("Execution Time %s seconds: " % (time.time() - start_time))

Snippet_382()

************How to setup a Simple RNN model for imdb sentiment analysis in Keras************

Using TensorFlow backend.

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(25000,)
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(25000, 500)
[[   0    0    0 ...   19  178   32]
 [   0    0    0 ...   16  145   95]
 [   0    0    0 ...    7  129  113]
 ...
 [   0    0    0 ...    4 3586    2]
 [   0    0    0 ...   12    9   23]
 [   0    0    0 ...  204  131    9]]

(25000, 500)
[[   0    0    0 ...   14    6  717]
 [   0    0    0 ...  125    4 3077]
 [  33    6   58 ...    9   57  975]
 ...
 [   0    0    0 ...   21  846    2]
 [   0    0    0 ... 2302    7  470]
 [   0    0    0 ...   34 2005 2643]]
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, 500, 32)           160000    
_________________________________________________________________
simple_rnn_1 (SimpleRNN)     (None, 512)               279040    
_________________________________________________________________
dense_1 (Dense)              (None, 500)               256500    
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 501       
=================================================================
Total params: 696,041
Trainable params: 696,041
Non-trainable params: 0
_________________________________________________________________

/Users/nilimesh/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "

Train on 25000 samples, validate on 25000 samples
Epoch 1/20
12416/25000 [=============>................] - ETA: 1:56 - loss: 1.4312 - accuracy: 0.5008