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24 lines
890 B
Python
24 lines
890 B
Python
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#Importing Libraries
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import numpy as np
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import tensorflow as tf
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import tensorflow.contrib.learn as learn
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import tensorflow.contrib.metrics as metrics
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#Importing Dataset
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mnist = learn.datasets.load_dataset('mnist')
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#Training Datasets
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data = mnist.train.images
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labels = np.asarray(mnist.train.labels, dtype=np.int32)
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test_data = mnist.test.images
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test_labels = np.asarray(mnist.test.labels, dtype=np.int32)
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feature_columns = learn.infer_real_valued_columns_from_input(data)
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#Applying Classifiers
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classifier = learn.DNNClassifier(feature_columns=feature_columns, n_classes=10,hidden_units=[1024,512,256])
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classifier.fit(data, labels, batch_size=100, steps=1000)
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#Evaluating the Results
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classifier.evaluate(test_data, test_labels)
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accuracy_score = classifier.evaluate(test_data, test_labels)["accuracy"]
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print('Accuracy: {0:f}'.format(accuracy_score))
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