diff --git a/keras_contrib/utils/test_utils.py b/keras_contrib/utils/test_utils.py
new file mode 100644
index 0000000..d333fa9
--- /dev/null
+++ b/keras_contrib/utils/test_utils.py
@@ -0,0 +1,159 @@
+"""Utilities related to Keras unit tests."""
+import numpy as np
+from numpy.testing import assert_allclose
+import inspect
+import six
+
+from keras.engine import Model, Input
+from keras.models import Sequential
+from keras.models import model_from_json
+from keras import backend as K
+
+
+def get_test_data(num_train=1000, num_test=500, input_shape=(10,),
+                  output_shape=(2,),
+                  classification=True, num_classes=2):
+    """Generates test data to train a model on.
+
+    classification=True overrides output_shape
+    (i.e. output_shape is set to (1,)) and the output
+    consists in integers in [0, num_class-1].
+
+    Otherwise: float output with shape output_shape.
+    """
+    samples = num_train + num_test
+    if classification:
+        y = np.random.randint(0, num_classes, size=(samples,))
+        X = np.zeros((samples,) + input_shape)
+        for i in range(samples):
+            X[i] = np.random.normal(loc=y[i], scale=0.7, size=input_shape)
+    else:
+        y_loc = np.random.random((samples,))
+        X = np.zeros((samples,) + input_shape)
+        y = np.zeros((samples,) + output_shape)
+        for i in range(samples):
+            X[i] = np.random.normal(loc=y_loc[i], scale=0.7, size=input_shape)
+            y[i] = np.random.normal(loc=y_loc[i], scale=0.7, size=output_shape)
+
+    return (X[:num_train], y[:num_train]), (X[num_train:], y[num_train:])
+
+
+def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None,
+               input_data=None, expected_output=None,
+               expected_output_dtype=None, fixed_batch_size=False, tolerance=1e-3):
+    """Test routine for a layer with a single input tensor
+    and single output tensor.
+    """
+    # generate input data
+    if input_data is None:
+        assert input_shape
+        if not input_dtype:
+            input_dtype = K.floatx()
+        input_data_shape = list(input_shape)
+        for i, e in enumerate(input_data_shape):
+            if e is None:
+                input_data_shape[i] = np.random.randint(1, 4)
+        input_data = (10 * np.random.random(input_data_shape))
+        input_data = input_data.astype(input_dtype)
+    else:
+        if input_shape is None:
+            input_shape = input_data.shape
+        if input_dtype is None:
+            input_dtype = input_data.dtype
+    if expected_output_dtype is None:
+        expected_output_dtype = input_dtype
+
+    # instantiation
+    layer = layer_cls(**kwargs)
+
+    # test get_weights , set_weights at layer level
+    weights = layer.get_weights()
+    layer.set_weights(weights)
+
+    # test and instantiation from weights
+    if 'weights' in inspect.getargspec(layer_cls.__init__):
+        kwargs['weights'] = weights
+        layer = layer_cls(**kwargs)
+
+    # test in functional API
+    if fixed_batch_size:
+        x = Input(batch_shape=input_shape, dtype=input_dtype)
+    else:
+        x = Input(shape=input_shape[1:], dtype=input_dtype)
+    y = layer(x)
+    assert K.dtype(y) == expected_output_dtype
+
+    # check shape inference
+    model = Model(x, y)
+    expected_output_shape = layer.compute_output_shape(input_shape)
+    actual_output = model.predict(input_data)
+    actual_output_shape = actual_output.shape
+    for expected_dim, actual_dim in zip(expected_output_shape,
+                                        actual_output_shape):
+        if expected_dim is not None:
+            assert expected_dim == actual_dim
+    if expected_output is not None:
+        assert_allclose(actual_output, expected_output, rtol=tolerance)
+
+    # test serialization, weight setting at model level
+    model_config = model.get_config()
+    recovered_model = Model.from_config(model_config)
+    if model.weights:
+        weights = model.get_weights()
+        recovered_model.set_weights(weights)
+        _output = recovered_model.predict(input_data)
+        assert_allclose(_output, actual_output, rtol=tolerance)
+
+    # test training mode (e.g. useful for dropout tests)
+    model.compile('rmsprop', 'mse')
+    model.train_on_batch(input_data, actual_output)
+
+    # test as first layer in Sequential API
+    layer_config = layer.get_config()
+    layer_config['batch_input_shape'] = input_shape
+    layer = layer.__class__.from_config(layer_config)
+
+    model = Sequential()
+    model.add(layer)
+    actual_output = model.predict(input_data)
+    actual_output_shape = actual_output.shape
+    for expected_dim, actual_dim in zip(expected_output_shape,
+                                        actual_output_shape):
+        if expected_dim is not None:
+            assert expected_dim == actual_dim
+    if expected_output is not None:
+        assert_allclose(actual_output, expected_output, rtol=1e-3)
+
+    # test serialization, weight setting at model level
+    model_config = model.get_config()
+    recovered_model = Sequential.from_config(model_config)
+    if model.weights:
+        weights = model.get_weights()
+        recovered_model.set_weights(weights)
+        _output = recovered_model.predict(input_data)
+        assert_allclose(_output, actual_output, rtol=1e-3)
+
+    # test training mode (e.g. useful for dropout tests)
+    model.compile('rmsprop', 'mse')
+    model.train_on_batch(input_data, actual_output)
+
+    # for further checks in the caller function
+    return actual_output
+
+
+def keras_test(func):
+    """Function wrapper to clean up after TensorFlow tests.
+
+    # Arguments
+        func: test function to clean up after.
+
+    # Returns
+        A function wrapping the input function.
+    """
+    @six.wraps(func)
+    def wrapper(*args, **kwargs):
+        output = func(*args, **kwargs)
+        if K.backend() == 'tensorflow':
+            K.clear_session()
+        return output
+    return wrapper