344f9c97e546d89d1e85179d8df522", "<KEY>", "<KEY>", "81ffca9c33c445eaab643e0c800a1c76", "<KEY>", "<KEY>", "22e59e58d2164f0fa1a6205bd9c78ecb", "<KEY>", "<KEY>", "81595aad97284ca98af5fddc539c3abb", "<KEY>", "f7d61094ddac4a538495a432a9369cc4", "<KEY>", "<KEY>", "<KEY>", "2bddd6c6bd1a492b8e5c5484ce836e37", "1a6cffe8d69f4e6e853b26a82b697dfe", "<KEY>", "<KEY>", "81ab92858c3246b0a47395c16e96a490", "<KEY>", "d5d7d589e81340aeb69808c402cbd1de", "d1517e7bd1664d37b8306845a71efe59", "<KEY>", "fb9d084d0f40431ba9dd808453f5e99b", "<KEY>", "1bf040744b7e404892eabe6ada0d20be", "2c1fa87c96774d1a86d20a99ed63df7b", "<KEY>", "<KEY>", "de11a09c6fe74780889f35162960af41", "<KEY>", "976a9b4e366346a49cd639a0fce7c602", "7b76f5ecb63e446e99c8e7abb932a533", "<KEY>", "<KEY>", "36b8a61e33c9466eb9c4d6422e76ff49", "290c0e9e96e04ae3ae67319476469dae", "<KEY>", "<KEY>", "<KEY>", "9363758241eb480daceca2e155c18dfd", "d4ed7e15e6e74d8ea78769137f934da9", "5c4f4408144547dcb693371ec3de080d"]} outputId="561dda87-6aa1-4696-ae52-2521c1626ce3"
# Get the data
dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True, as_supervised=True)
train_dataset, test_dataset = dataset['train'], dataset['test']


# + colab_type="code" id="L11bIR6-PKvs" colab={}
tokenizer = info.features['text'].encoder

# + colab_type="code" id="ffvRUI0_McDS" colab={"base_uri": "https://localhost:8080/", "height": 141} outputId="e9db682f-59de-4a53-b341-3dc8e7b462b4"
BUFFER_SIZE = 10000
BATCH_SIZE = 64

train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.padded_batch(BATCH_SIZE, train_dataset.output_shapes)
test_dataset = test_dataset.padded_batch(BATCH_SIZE, test_dataset.output_shapes)

# + colab_type="code" id="jo1jjO3vn0jo" colab={}
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(tokenizer.vocab_size, 64),
    tf.keras.layers.Conv1D(128, 5, activation='relu'),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# + colab_type="code" id="QKI5dfPgMioL" colab={"base_uri": "https://localhost:8080/", "height": 330} outputId="d69a0979-7bf6-422d-f4ef-c14e56ef279e"
model.summary()

# + colab_type="code" id="Uip7QOVzMoMq" colab={}
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# + colab_type="code" id="7mlgzaRDMtF6" colab={"base_uri": "https://localhost:8080/", "height": 364} outputId="77739bb6-5248-42c0-cb40-ce2d5d49beae"
NUM_EPOCHS = 10
history = model.fit(train_dataset, epochs=NUM_EPOCHS, validation_data=test_dataset)

# + colab_type="code" id="Mp1Z7P9pYRSK" colab={}
import matplotlib.pyplot as plt


def plot_graphs(history, string):
  plt.plot(history.history[string])
  plt.plot(history.history['val_'+string])
  plt.xlabel("Epochs")
  plt.ylabel(string)
  plt.legend([string, 'val_'+string])
  plt.show()


# + colab_type="code" id="R_sX6ilIM515" colab={"base_uri": "https://localhost:8080/", "height": 282} outputId="c3cb987f-f8d6-4237-e0f1-6d00badda0b2"
plot_graphs(history, 'accuracy')

# + colab_type="code" id="RFEXtKtqNARB" colab={"base_uri": "https://localhost:8080/", "height": 282} outputId="f97d6778-bc17-4df4-903c-93d627b1ec2f"
plot_graphs(history, 'loss')
With the closure of Molly Bloom's, U of T students found themselves temporarily short a student-friendly venue on College Street. Sweeping into the fill that gap is a familiar face in the form of this Green Room / Red Room / Pour Boy clone. Will its cheap eats and drinks draw a crowd?
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Cheat Sheet: March 6, 2013
Wind knocks power out for 15 thousand customers
Officials say power was knocked out for 15,000 Virginia Beach residents today when high winds caused a tree to fall on a transmission line near the intersection of Northampton Boulevard and Bayside Drive just before 4 p.m. Areas affected include Stratford, Shore Drive and Independence Boulevard. Crews are en route, and it is not known when power will be restored. (Source)
Arkansas adopts strictest abortion law in the U.S.
Today Arkansas adopted.” (Source)
Teacher’s remarks prompt police call in Norfolk
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