neural networks classification python

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Nicolás
07 Nov 2020
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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Roosevelt
18 Sep 2019
1# first neural network with keras tutorial
2from numpy import loadtxt
3from keras.models import Sequential
4from keras.layers import Dense
5# load the dataset
6dataset = loadtxt('pima-indians-diabetes.csv', delimiter=',')
7# split into input (X) and output (y) variables
8X = dataset[:,0:8]
9y = dataset[:,8]
10# define the keras model
11model = Sequential()
12model.add(Dense(12, input_dim=8, activation='relu'))
13model.add(Dense(8, activation='relu'))
14model.add(Dense(1, activation='sigmoid'))
15# compile the keras model
16model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
17# fit the keras model on the dataset
18model.fit(X, y, epochs=150, batch_size=10)
19# evaluate the keras model
20_, accuracy = model.evaluate(X, y)
21print('Accuracy: %.2f' % (accuracy*100))
22
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