implemented mnist using DNN

AAN/arti_neural_net.py

@@ -13,6 +13,38 @@
 y_pred = per_clf.predict([[2,0.5]])
 print(y_pred) 
 
+
 #%%
 import tensorflow as tf
 
+(X_train , Y_train) , (X_test,Y_test) = tf.keras.datasets.mnist.load_data()
+print(X_train.shape,X_test.shape)
+X_train = X_train.astype(np.float32).reshape(-1,28*28)/255.0
+X_test = X_test.astype(np.float32).reshape(-1,28*28)/255.0
+Y_train = Y_train.astype(np.int32)
+Y_test = Y_test.astype(np.int32)
+print(X_train.shape,X_test.shape)
+X_valid , X_train = X_train[:5000],X_train[5000:]
+Y_valid , Y_train = Y_train[:5000],Y_train[5000:]
+
+print(X_train)
+
+#%%
+feature_cols = [tf.feature_column.numeric_column(
+    "X",shape=[28*28])]
+dnn_clf = tf.estimator.DNNClassifier(hidden_units=
+[300,100],n_classes = 10,feature_columns = feature_cols)
+
+input_fn = tf.estimator.inputs.numpy_input_fn(
+    x = {"X":X_train},y=Y_train,num_epochs=40,
+    batch_size=50, shuffle = True
+)
+dnn_clf.train(input_fn=input_fn)
+
+#%%
+test_input_fn = tf.estimator.inputs.numpy_input_fn(
+    x={"X":X_test},y=Y_test,shuffle=False
+)
+eval_results = dnn_clf.evaluate(input_fn=test_input_fn)
+
+print(eval_results)