coderApes
diff --git a/‎real world examples/bank_marketing_learning/bank_marketing.py renamed to ‎real world examples/bank_marketing_learning/bank_marketing_sk.py b/‎real world examples/bank_marketing_learning/bank_marketing.py renamed to ‎real world examples/bank_marketing_learning/bank_marketing_sk.py
diff --git a/‎real world examples/bank_marketing_learning/bank_marketing_tfnn.py
Lines changed: 24 additions & 0 deletions b/‎real world examples/bank_marketing_learning/bank_marketing_tfnn.py
Lines changed: 24 additions & 0 deletions
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+import tfnn
+import pandas as pd
+
+bank_data = pd.read_csv('bank-full.csv', sep=';')
+
+data = tfnn.Data(bank_data.iloc[:, :-1], bank_data.iloc[:, -1])
+data.encode_cat_y(inplace=True)
+data.encode_cat_x(inplace=True)
+network = tfnn.ClfNetwork(data.xs.shape[1], data.ys.shape[1],)
+data = network.normalizer.minmax_fit(data, -1, 1)
+train_data, test_data = data.train_test_split()
+network.add_hidden_layer(50, activator=tfnn.nn.relu)
+network.add_output_layer(activator=None)
+network.set_optimizer(tfnn.train.GradientDescentOptimizer(0.0001))
+evaluator = tfnn.Evaluator(network)
+
+for i in range(1000):
+    b_xs, b_ys = train_data.next_batch(100, loop=True)
+    network.run_step(b_xs, b_ys)
+    if i % 50 == 0:
+        print(evaluator.compute_accuracy(test_data.xs, test_data.ys))
+# print(test_data.ys.iloc[:,0].value_counts())
+print(network.predict(test_data.xs.iloc[20:30, :]))
+print(test_data.ys.iloc[20:30, :])