Add tests for inputs

tests/test_inputs.py

@@ -0,0 +1,58 @@
+'''
+    This file contains test cases for tflearn
+'''
+
+import tensorflow as tf
+import tflearn
+import unittest
+
+class TestInputs(unittest.TestCase):
+    '''
+        This class contains test cases for serval input types
+    '''
+    INPUT_DATA_1 = [ [ 1 ], [ 2 ], [ 3 ], [ 4 ], [ 5 ] ]
+    INPUT_DATA_2 = [ [ 6 ], [ 7 ], [ 8 ], [ 9 ], [ 10 ] ]
+    TARGET = [ [ 14 ], [ 18 ], [ 22 ], [ 26 ], [ 30 ] ]   # (input1 + input2) * 2
+
+    def test_list_inputs(self):
+        """Test input a list
+        """
+        with tf.Graph().as_default():
+            model, inputs, target = self.build_simple_model()
+            model.fit([ inpData for _, _, inpData in inputs ], target, batch_size = 1)
+
+    def test_dict_inputs(self):
+        """Test input a dict with layer name
+        """
+        with tf.Graph().as_default():
+            model, inputs, target = self.build_simple_model()
+            model.fit({ name: inpData for name, _, inpData in inputs }, target, batch_size = 1)
+
+    def test_dict_withtensor_inputs(self):
+        """Test input a dict with placeholder
+        """
+        with tf.Graph().as_default():
+            model, inputs, target = self.build_simple_model()
+            model.fit({ placeholder: inpData for _, placeholder, inpData in inputs }, target, batch_size = 1)
+
+    def build_simple_model(self):
+        """Build a simple model for test
+        Returns:
+            DNN, [ (input layer name, input placeholder, input data) ], Target data
+        """
+        inputPlaceholder1, inputPlaceholder2 = \
+            tf.placeholder(tf.float32, (1, 1), name = "input1"), tf.placeholder(tf.float32, (1, 1), name = "input2")
+        input1 = tflearn.input_data(placeholder = inputPlaceholder1)
+        input2 = tflearn.input_data(placeholder = inputPlaceholder2)
+        network = tflearn.merge([ input1, input2 ], "sum")
+        network = tflearn.reshape(network, (1, 1))
+        network = tflearn.fully_connected(network, 1)
+        network = tflearn.regression(network)
+        return (
+            tflearn.DNN(network),
+            [ ("input1:0", inputPlaceholder1, self.INPUT_DATA_1), ("input2:0", inputPlaceholder2, self.INPUT_DATA_2) ],
+            self.TARGET,
+        )
+
+if __name__ == "__main__":
+    unittest.main()