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Katerina
12 Jan 2019
1import numpy as np
2from keras.utils import to_categorical
3from keras import models
4from keras import layers
5from keras.datasets import imdb
6(training_data, training_targets), (testing_data, testing_targets) = imdb.load_data(num_words=10000)
7data = np.concatenate((training_data, testing_data), axis=0)
8targets = np.concatenate((training_targets, testing_targets), axis=0)
9def vectorize(sequences, dimension = 10000):
10 results = np.zeros((len(sequences), dimension))
11 for i, sequence in enumerate(sequences):
12  results[i, sequence] = 1
13 return results
14 
15data = vectorize(data)
16targets = np.array(targets).astype("float32")
17test_x = data[:10000]
18test_y = targets[:10000]
19train_x = data[10000:]
20train_y = targets[10000:]
21model = models.Sequential()
22# Input - Layer
23model.add(layers.Dense(50, activation = "relu", input_shape=(10000, )))
24# Hidden - Layers
25model.add(layers.Dropout(0.3, noise_shape=None, seed=None))
26model.add(layers.Dense(50, activation = "relu"))
27model.add(layers.Dropout(0.2, noise_shape=None, seed=None))
28model.add(layers.Dense(50, activation = "relu"))
29# Output- Layer
30model.add(layers.Dense(1, activation = "sigmoid"))
31model.summary()
32# compiling the model
33model.compile(
34 optimizer = "adam",
35 loss = "binary_crossentropy",
36 metrics = ["accuracy"]
37)
38results = model.fit(
39 train_x, train_y,
40 epochs= 2,
41 batch_size = 500,
42 validation_data = (test_x, test_y)
43)
44print("Test-Accuracy:", np.mean(results.history["val_acc"]))
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