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Handwriting_Recognizer_DNN_classifier.py

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

distance.py

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+import geocoder
+g = geocoder.ipinfo('me')
+print(g.latlng)
+

locator.py

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+import geocoder
+t=input("enter the location:")
+g = geocoder.arcgis(t)
+print(g.latlng)
+