From bc2381a98d2d0dcad5df0710ffef1097bba2e7cf Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 17:29:07 +0200
Subject: [PATCH 01/17] Moved tests out for CNN and ConvLSTM models

---
 tests/test_modelgen.py               | 124 ---------------------------
 tests/tests_models/test_cnn.py       |  98 +++++++++++++++++++++
 tests/tests_models/test_conv_lstm.py |  51 +++++++++++
 3 files changed, 149 insertions(+), 124 deletions(-)
 create mode 100755 tests/tests_models/test_cnn.py
 create mode 100755 tests/tests_models/test_conv_lstm.py

diff --git a/tests/test_modelgen.py b/tests/test_modelgen.py
index 8604ef2..a96d22a 100644
--- a/tests/test_modelgen.py
+++ b/tests/test_modelgen.py
@@ -51,131 +51,7 @@ def _generate_train_data(self, x_shape, nr_classes):
         y_train = np.random.randint(0, 1, size=(1, nr_classes))
         return X_train, y_train
 
-    # Tests for CNN model:
-    def test_cnn_starts_with_batchnorm(self):
-        """ CNN models should always start with a batch normalization layer. """
-        model_type = CNN((None, 20, 3), 2)
-        model = model_type.create_model(**{"filters": [32, 32],
-                                           "fc_hidden_nodes": 100})
-        assert 'BatchNormalization' in str(type(model.layers[0])), 'Wrong layer type.'
-
-    def test_cnn_fc_nodes(self):
-        """ CNN model should have number of dense nodes defined by user. """
-        fc_hidden_nodes = 101
-        model_type = CNN((None, 20, 3), 2)
-        model = model_type.create_model(**{"filters": [32, 32],
-                                           "fc_hidden_nodes": fc_hidden_nodes})
-
-        dense_layer = [l for l in model.layers if 'Dense' in str(l)][0]
-        assert dense_layer.output_shape[1] == fc_hidden_nodes, 'Wrong number of fc nodes.'
-
-    def test_cnn_batchnorm_dim(self):
-        """"The output shape of the batchnorm should be (None, nr_timesteps, nr_filters)"""
-        model_type = CNN((None, 20, 3), 2)
-        model = model_type.create_model(**{"filters": [32, 32],
-                                           "fc_hidden_nodes": 100})
-
-        batchnormlay = model.layers[2]
-        assert batchnormlay.output_shape == (None, 20, 32)
-
-    def test_cnn_enough_batchnorm(self):
-        """CNN model should contain as many batch norm layers as it has activations layers"""
-        model_type = CNN((None, 20, 3), 2)
-        model = model_type.create_model(**{"filters": [32, 32],
-                                           "fc_hidden_nodes": 100})
-
-        batch_norm_layers = len([l for l in model.layers if 'BatchNormalization' in str(l)])
-        activation_layers = len([l for l in model.layers if 'Activation' in str(l)])
-        assert batch_norm_layers == activation_layers
-
-    def test_cnn_metrics(self):
-        """CNN model should be compiled with the metrics that we give it"""
-        metrics = ['accuracy', 'mae']
-        x_shape = (None, 20, 3)
-        nr_classes = 2
-        X_train, y_train = self._generate_train_data(x_shape, nr_classes)
-
-        model_type = CNN(x_shape, nr_classes, metrics=metrics)
-        model = model_type.create_model(**{"filters": [32, 32],
-                                           "fc_hidden_nodes": 100})
-        model.fit(X_train, y_train, epochs=1)
-
-        model_metrics = [m.name for m in model.metrics]
-        for metric in metrics:
-            assert metric in model_metrics
-
-    def test_CNN_hyperparameters_nrlayers(self):
-        """ Number of Conv layers from range [4, 4] should be 4. """
-        custom_settings = get_default()
-        kwargs = {'cnn_min_layers': 4,
-                  'cnn_max_layers': 4}
-        # Replace default parameters with input
-        for key, value in kwargs.items():
-            if key in custom_settings:
-                custom_settings[key] = value
-
-        model_type = CNN(None, None, **custom_settings)
-        hyperparams = model_type.generate_hyperparameters()
-
-        assert len(hyperparams.get('filters')) == 4
-
-    def test_CNN_hyperparameters_fcnodes(self):
-        """ Number of fc nodes from range [123, 123] should be 123. """
-        custom_settings = get_default()
-        kwargs = {'cnn_min_fc_nodes': 123,
-                  'cnn_max_fc_nodes': 123}
-        # Replace default parameters with input
-        for key, value in kwargs.items():
-            if key in custom_settings:
-                custom_settings[key] = value
-
-        model_type = CNN(None, None, **custom_settings)
-        hyperparams = model_type.generate_hyperparameters()
-
-        assert hyperparams.get('fc_hidden_nodes') == 123
-
-    # Tests for DeepconvLSTM model:
-    def test_deepconvlstm_batchnorm_dim(self):
-        """The output shape of the batchnorm should be (None, nr_timesteps, nr_channels, nr_filters)"""
-        model_type = ConvLSTM((None, 20, 3), 2)
-        model = model_type.create_model(**{"filters": [32, 32],
-                                           "lstm_dims": [32, 32]})
-
-        batchnormlay = model.layers[3]
-        assert batchnormlay.output_shape == (None, 20, 3, 32)
-
-    def test_deepconvlstm_enough_batchnorm(self):
-        """LSTM model should contain as many batch norm layers as it has activations layers"""
-        model_type = ConvLSTM((None, 20, 3), 2)
-        model = model_type.create_model(**{"filters": [32, 32, 32],
-                                           "lstm_dims": [32, 32, 32]})
-
-        batch_norm_layers = len([l for l in model.layers if 'BatchNormalization' in str(l)])
-        activation_layers = len([l for l in model.layers if 'Activation' in str(l)])
-        assert batch_norm_layers == activation_layers
-
-    def test_DeepConvLSTM_hyperparameters_nrconvlayers(self):
-        """ Number of Conv layers from range [4, 4] should be 4. """
-        custom_settings = get_default()
-        kwargs = {'deepconvlstm_min_conv_layers': 4,
-                  'deepconvlstm_max_conv_layers': 4}
-        # Replace default parameters with input
-        for key, value in kwargs.items():
-            if key in custom_settings:
-                custom_settings[key] = value
-
-        model_type = ConvLSTM(None, None, **custom_settings)
-        hyperparams = model_type.generate_hyperparameters()
-
-        assert len(hyperparams.get('filters')) == 4
-
-    def test_deepconvlstm_starts_with_batchnorm(self):
-        """ DeepConvLSTM models should always start with a batch normalization layer. """
-        model_type = ConvLSTM((None, 20, 3), 2)
-        model = model_type.create_model(**{"filters": [32, 32],
-                                           "lstm_dims": [32, 32]})
 
-        assert 'BatchNormalization' in str(type(model.layers[0])), 'Wrong layer type.'
 
     # Tests for ResNet model:
     def test_ResNet_starts_with_batchnorm(self):
diff --git a/tests/tests_models/test_cnn.py b/tests/tests_models/test_cnn.py
new file mode 100755
index 0000000..2897d26
--- /dev/null
+++ b/tests/tests_models/test_cnn.py
@@ -0,0 +1,98 @@
+# -*- coding: utf-8 -*-
+import unittest
+from mcfly.models import CNN
+from tests.test_modelgen import get_default
+
+
+class CNNSuite(unittest.TestCase):
+    """
+    Test cases for CNN models.
+    """
+
+    def test_cnn_starts_with_batchnorm(self):
+        """ CNN models should always start with a batch normalization layer. """
+        model_type = CNN((None, 20, 3), 2)
+        model = model_type.create_model(**{"filters": [32, 32],
+                                           "fc_hidden_nodes": 100})
+        assert 'BatchNormalization' in str(type(model.layers[0])), 'Wrong layer type.'
+
+
+    def test_cnn_fc_nodes(self):
+        """ CNN model should have number of dense nodes defined by user. """
+        fc_hidden_nodes = 101
+        model_type = CNN((None, 20, 3), 2)
+        model = model_type.create_model(**{"filters": [32, 32],
+                                           "fc_hidden_nodes": fc_hidden_nodes})
+
+        dense_layer = [l for l in model.layers if 'Dense' in str(l)][0]
+        assert dense_layer.output_shape[1] == fc_hidden_nodes, 'Wrong number of fc nodes.'
+
+
+    def test_cnn_batchnorm_dim(self):
+        """"The output shape of the batchnorm should be (None, nr_timesteps, nr_filters)"""
+        model_type = CNN((None, 20, 3), 2)
+        model = model_type.create_model(**{"filters": [32, 32],
+                                           "fc_hidden_nodes": 100})
+
+        batchnormlay = model.layers[2]
+        assert batchnormlay.output_shape == (None, 20, 32)
+
+
+    def test_cnn_enough_batchnorm(self):
+        """CNN model should contain as many batch norm layers as it has activations layers"""
+        model_type = CNN((None, 20, 3), 2)
+        model = model_type.create_model(**{"filters": [32, 32],
+                                           "fc_hidden_nodes": 100})
+
+        batch_norm_layers = len([l for l in model.layers if 'BatchNormalization' in str(l)])
+        activation_layers = len([l for l in model.layers if 'Activation' in str(l)])
+        assert batch_norm_layers == activation_layers
+
+
+    def test_cnn_metrics(self):
+        """CNN model should be compiled with the metrics that we give it"""
+        metrics = ['accuracy', 'mae']
+        x_shape = (None, 20, 3)
+        nr_classes = 2
+        X_train, y_train = self._generate_train_data(x_shape, nr_classes)
+
+        model_type = CNN(x_shape, nr_classes, metrics=metrics)
+        model = model_type.create_model(**{"filters": [32, 32],
+                                           "fc_hidden_nodes": 100})
+        model.fit(X_train, y_train, epochs=1)
+
+        model_metrics = [m.name for m in model.metrics]
+        for metric in metrics:
+            assert metric in model_metrics
+
+
+    def test_CNN_hyperparameters_nrlayers(self):
+        """ Number of Conv layers from range [4, 4] should be 4. """
+        custom_settings = get_default()
+        kwargs = {'cnn_min_layers': 4,
+                  'cnn_max_layers': 4}
+        # Replace default parameters with input
+        for key, value in kwargs.items():
+            if key in custom_settings:
+                custom_settings[key] = value
+
+        model_type = CNN(None, None, **custom_settings)
+        hyperparams = model_type.generate_hyperparameters()
+
+        assert len(hyperparams.get('filters')) == 4
+
+
+    def test_CNN_hyperparameters_fcnodes(self):
+        """ Number of fc nodes from range [123, 123] should be 123. """
+        custom_settings = get_default()
+        kwargs = {'cnn_min_fc_nodes': 123,
+                  'cnn_max_fc_nodes': 123}
+        # Replace default parameters with input
+        for key, value in kwargs.items():
+            if key in custom_settings:
+                custom_settings[key] = value
+
+        model_type = CNN(None, None, **custom_settings)
+        hyperparams = model_type.generate_hyperparameters()
+
+        assert hyperparams.get('fc_hidden_nodes') == 123
diff --git a/tests/tests_models/test_conv_lstm.py b/tests/tests_models/test_conv_lstm.py
new file mode 100755
index 0000000..9e9fa2f
--- /dev/null
+++ b/tests/tests_models/test_conv_lstm.py
@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+import unittest
+from mcfly.models import ConvLSTM
+from tests.test_modelgen import get_default
+
+class ConvLSTMSuite(unittest.TestCase):
+    """
+    Tests cases for DeepconvLSTM models.
+    """
+
+    def test_deepconvlstm_batchnorm_dim(self):
+        """The output shape of the batchnorm should be (None, nr_timesteps, nr_channels, nr_filters)"""
+        model_type = ConvLSTM((None, 20, 3), 2)
+        model = model_type.create_model(**{"filters": [32, 32],
+                                           "lstm_dims": [32, 32]})
+
+        batchnormlay = model.layers[3]
+        assert batchnormlay.output_shape == (None, 20, 3, 32)
+
+    def test_deepconvlstm_enough_batchnorm(self):
+        """LSTM model should contain as many batch norm layers as it has activations layers"""
+        model_type = ConvLSTM((None, 20, 3), 2)
+        model = model_type.create_model(**{"filters": [32, 32, 32],
+                                           "lstm_dims": [32, 32, 32]})
+
+        batch_norm_layers = len([l for l in model.layers if 'BatchNormalization' in str(l)])
+        activation_layers = len([l for l in model.layers if 'Activation' in str(l)])
+        assert batch_norm_layers == activation_layers
+
+    def test_DeepConvLSTM_hyperparameters_nrconvlayers(self):
+        """ Number of Conv layers from range [4, 4] should be 4. """
+        custom_settings = get_default()
+        kwargs = {'deepconvlstm_min_conv_layers': 4,
+                  'deepconvlstm_max_conv_layers': 4}
+        # Replace default parameters with input
+        for key, value in kwargs.items():
+            if key in custom_settings:
+                custom_settings[key] = value
+
+        model_type = ConvLSTM(None, None, **custom_settings)
+        hyperparams = model_type.generate_hyperparameters()
+
+        assert len(hyperparams.get('filters')) == 4
+
+    def test_deepconvlstm_starts_with_batchnorm(self):
+        """ DeepConvLSTM models should always start with a batch normalization layer. """
+        model_type = ConvLSTM((None, 20, 3), 2)
+        model = model_type.create_model(**{"filters": [32, 32],
+                                           "lstm_dims": [32, 32]})
+
+        assert 'BatchNormalization' in str(type(model.layers[0])), 'Wrong layer type.'

From f9bae2dd59619a2825caa6f21da4396e880d8582 Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 17:32:14 +0200
Subject: [PATCH 02/17] Moved function to a public spot

---
 tests/test_modelgen.py | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/tests/test_modelgen.py b/tests/test_modelgen.py
index a96d22a..9ee5603 100644
--- a/tests/test_modelgen.py
+++ b/tests/test_modelgen.py
@@ -43,15 +43,17 @@ def get_default():
                 'high_reg': 4}
     return settings
 
-class ModelGenerationSuite(unittest.TestCase):
-    """Basic test cases."""
 
-    def _generate_train_data(self, x_shape, nr_classes):
+# TODO: Move this to an utils file?
+def generate_train_data(x_shape, nr_classes):
         X_train = np.random.rand(1, *x_shape[1:])
         y_train = np.random.randint(0, 1, size=(1, nr_classes))
         return X_train, y_train
 
 
+class ModelGenerationSuite(unittest.TestCase):
+    """Basic test cases."""
+
 
     # Tests for ResNet model:
     def test_ResNet_starts_with_batchnorm(self):
@@ -100,7 +102,7 @@ def test_ResNet_metrics(self):
         metrics = ['accuracy', 'mae']
         x_shape = (None, 20, 3)
         nr_classes = 2
-        X_train, y_train = self._generate_train_data(x_shape, nr_classes)
+        X_train, y_train = generate_train_data(x_shape, nr_classes)
 
         model_type = ResNet(x_shape, nr_classes, metrics=metrics)
         model = model_type.create_model(16, 20)
@@ -177,7 +179,7 @@ def test_InceptionTime_metrics(self):
         metrics = ['accuracy', 'mae']
         x_shape = (None, 20, 3)
         nr_classes = 2
-        X_train, y_train = self._generate_train_data(x_shape, nr_classes)
+        X_train, y_train = generate_train_data(x_shape, nr_classes)
 
         model_type = InceptionTime(x_shape, nr_classes, metrics=metrics)
         model = model_type.create_model(16)
@@ -216,7 +218,7 @@ def test_generate_models_metrics(self):
         """ Test if correct number of models is generated and if metrics is correct. """
         x_shape = (None, 20, 10)
         nr_classes = 2
-        X_train, y_train = self._generate_train_data(x_shape, nr_classes)
+        X_train, y_train = generate_train_data(x_shape, nr_classes)
         n_models = 5
 
         models = modelgen.generate_models(x_shape, nr_classes, n_models)
@@ -234,7 +236,7 @@ def test_generate_models_pass_model_object(self):
         """ Test if model class can be passed as model_types input."""
         x_shape = (None, 20, 10)
         nr_classes = 2
-        X_train, y_train = self._generate_train_data(x_shape, nr_classes)
+        X_train, y_train = generate_train_data(x_shape, nr_classes)
         n_models = 4
 
         models = modelgen.generate_models(x_shape, nr_classes, n_models,
@@ -250,7 +252,7 @@ def test_generate_models_exception(self):
         """ Test expected generate_models exception."""
         x_shape = (None, 20, 10)
         nr_classes = 2
-        X_train, y_train = self._generate_train_data(x_shape, nr_classes)
+        X_train, y_train = generate_train_data(x_shape, nr_classes)
         n_models = 2
 
         with pytest.raises(NameError, match="Unknown model name, 'wrong_entry'."):

From 5d126091d2da7e418e943ea2f9bc13f630a4fb78 Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 17:35:44 +0200
Subject: [PATCH 03/17] Moved out tests for ResNet

---
 tests/test_modelgen.py            | 79 ---------------------------
 tests/tests_models/test_resnet.py | 89 +++++++++++++++++++++++++++++++
 2 files changed, 89 insertions(+), 79 deletions(-)
 create mode 100755 tests/tests_models/test_resnet.py

diff --git a/tests/test_modelgen.py b/tests/test_modelgen.py
index 9ee5603..e915d05 100644
--- a/tests/test_modelgen.py
+++ b/tests/test_modelgen.py
@@ -55,85 +55,6 @@ class ModelGenerationSuite(unittest.TestCase):
     """Basic test cases."""
 
 
-    # Tests for ResNet model:
-    def test_ResNet_starts_with_batchnorm(self):
-        """ ResNet models should always start with a batch normalization layer. """
-        model_type = ResNet((None, 20, 3), 2)
-        model = model_type.create_model(16, 20)
-
-        assert 'BatchNormalization' in str(type(model.layers[1])), 'Wrong layer type.'
-
-
-    def test_ResNet_first_sandwich_layers(self):
-        """ ResNet models should always start with a residual module. """
-        model_type = ResNet((None, 20, 3), 2)
-        model = model_type.create_model(16, 20)
-
-        assert 'Conv1D' or 'Convolution1D' in str(type(model.layers[2])), 'Wrong layer type.'
-        assert 'BatchNormalization' in str(type(model.layers[3])), 'Wrong layer type.'
-        assert 'ReLU' in str(type(model.layers[4])), 'Wrong layer type.'
-
-
-    def test_ResNet_depth(self):
-        """ ResNet model should have depth (number of residual modules) as defined by user. """
-        depths = 2
-
-        model_type = ResNet((None, 20, 3), 2)
-        model = model_type.create_model(16, 20, network_depth=depths)
-
-        add_layers = [str(type(l)) for l in model.layers if 'Add' in str(type(l))]
-        assert len(add_layers) == depths, 'Wrong number of residual modules (network depths).'
-
-
-    def test_ResNet_first_module_dim(self):
-        """"The output shape throughout the first residual module should be (None, nr_timesteps, min_filters_number)"""
-        min_filters_number = 16
-
-        model_type = ResNet((None, 30, 5), 2)
-        model = model_type.create_model(min_filters_number, 20)
-
-        firstConvlayer = model.layers[2]
-        firstAddlayer = model.layers[12]
-        assert firstConvlayer.output_shape == (None, 30, min_filters_number)
-        assert firstAddlayer.output_shape == (None, 30, min_filters_number)
-
-    def test_ResNet_metrics(self):
-        """ResNet model should be compiled with the metrics that we give it"""
-        metrics = ['accuracy', 'mae']
-        x_shape = (None, 20, 3)
-        nr_classes = 2
-        X_train, y_train = generate_train_data(x_shape, nr_classes)
-
-        model_type = ResNet(x_shape, nr_classes, metrics=metrics)
-        model = model_type.create_model(16, 20)
-        model.fit(X_train, y_train, epochs=1)
-
-        model_metrics = [m.name for m in model.metrics]
-        for metric in metrics:
-            assert metric in model_metrics
-
-    def test_ResNet_hyperparameters(self):
-        """ Network depth from range [4,4] should be 4.
-        Maximum kernal size from range [10, 10] should be 10.
-        Minimum filter number from range [16, 16] should be 16.  """
-        custom_settings = get_default()
-        kwargs = {'resnet_min_network_depth': 4,
-                  'resnet_mmax_network_depth': 4,
-                  'resnet_min_max_kernel_size': 10,
-                  'resnet_max_max_kernel_size': 10,
-                  'resnet_min_filters_number': 16,
-                  'resnet_max_filters_number': 16}
-        # Replace default parameters with input
-        for key, value in kwargs.items():
-            if key in custom_settings:
-                custom_settings[key] = value
-
-        model_type = ResNet(None, None, **custom_settings)
-        hyperparams = model_type.generate_hyperparameters()
-
-        assert hyperparams.get('network_depth') == 4, 'Wrong network depth'
-        assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
-        assert hyperparams.get('min_filters_number') == 16, 'Wrong filter number'
 
     # Tests for InceptionTime model:
     def test_InceptionTime_starts_with_batchnorm(self):
diff --git a/tests/tests_models/test_resnet.py b/tests/tests_models/test_resnet.py
new file mode 100755
index 0000000..31ebe37
--- /dev/null
+++ b/tests/tests_models/test_resnet.py
@@ -0,0 +1,89 @@
+# -*- coding: utf-8 -*-
+
+import unittest
+from mcfly.models import ResNet
+from tests.test_modelgen import get_default, generate_train_data
+
+class ResNetSuite(unittest.TestCase):
+    """
+    Test cases for ResNet models.
+    """
+
+    def test_ResNet_starts_with_batchnorm(self):
+        """ ResNet models should always start with a batch normalization layer. """
+        model_type = ResNet((None, 20, 3), 2)
+        model = model_type.create_model(16, 20)
+
+        assert 'BatchNormalization' in str(type(model.layers[1])), 'Wrong layer type.'
+
+
+    def test_ResNet_first_sandwich_layers(self):
+        """ ResNet models should always start with a residual module. """
+        model_type = ResNet((None, 20, 3), 2)
+        model = model_type.create_model(16, 20)
+
+        assert 'Conv1D' or 'Convolution1D' in str(type(model.layers[2])), 'Wrong layer type.'
+        assert 'BatchNormalization' in str(type(model.layers[3])), 'Wrong layer type.'
+        assert 'ReLU' in str(type(model.layers[4])), 'Wrong layer type.'
+
+
+    def test_ResNet_depth(self):
+        """ ResNet model should have depth (number of residual modules) as defined by user. """
+        depths = 2
+
+        model_type = ResNet((None, 20, 3), 2)
+        model = model_type.create_model(16, 20, network_depth=depths)
+
+        add_layers = [str(type(l)) for l in model.layers if 'Add' in str(type(l))]
+        assert len(add_layers) == depths, 'Wrong number of residual modules (network depths).'
+
+
+    def test_ResNet_first_module_dim(self):
+        """"The output shape throughout the first residual module should be (None, nr_timesteps, min_filters_number)"""
+        min_filters_number = 16
+
+        model_type = ResNet((None, 30, 5), 2)
+        model = model_type.create_model(min_filters_number, 20)
+
+        firstConvlayer = model.layers[2]
+        firstAddlayer = model.layers[12]
+        assert firstConvlayer.output_shape == (None, 30, min_filters_number)
+        assert firstAddlayer.output_shape == (None, 30, min_filters_number)
+
+    def test_ResNet_metrics(self):
+        """ResNet model should be compiled with the metrics that we give it"""
+        metrics = ['accuracy', 'mae']
+        x_shape = (None, 20, 3)
+        nr_classes = 2
+        X_train, y_train = generate_train_data(x_shape, nr_classes)
+
+        model_type = ResNet(x_shape, nr_classes, metrics=metrics)
+        model = model_type.create_model(16, 20)
+        model.fit(X_train, y_train, epochs=1)
+
+        model_metrics = [m.name for m in model.metrics]
+        for metric in metrics:
+            assert metric in model_metrics
+
+    def test_ResNet_hyperparameters(self):
+        """ Network depth from range [4,4] should be 4.
+        Maximum kernal size from range [10, 10] should be 10.
+        Minimum filter number from range [16, 16] should be 16.  """
+        custom_settings = get_default()
+        kwargs = {'resnet_min_network_depth': 4,
+                  'resnet_mmax_network_depth': 4,
+                  'resnet_min_max_kernel_size': 10,
+                  'resnet_max_max_kernel_size': 10,
+                  'resnet_min_filters_number': 16,
+                  'resnet_max_filters_number': 16}
+        # Replace default parameters with input
+        for key, value in kwargs.items():
+            if key in custom_settings:
+                custom_settings[key] = value
+
+        model_type = ResNet(None, None, **custom_settings)
+        hyperparams = model_type.generate_hyperparameters()
+
+        assert hyperparams.get('network_depth') == 4, 'Wrong network depth'
+        assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
+        assert hyperparams.get('min_filters_number') == 16, 'Wrong filter number'

From 284fafd056f0886b25cdefb5a61b1c152ba25aa5 Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 17:38:43 +0200
Subject: [PATCH 04/17] InceptionTime was named as ResNet in some comments

---
 tests/test_modelgen.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/tests/test_modelgen.py b/tests/test_modelgen.py
index e915d05..c3c0633 100644
--- a/tests/test_modelgen.py
+++ b/tests/test_modelgen.py
@@ -55,7 +55,6 @@ class ModelGenerationSuite(unittest.TestCase):
     """Basic test cases."""
 
 
-
     # Tests for InceptionTime model:
     def test_InceptionTime_starts_with_batchnorm(self):
         """ InceptionTime models should always start with a batch normalization layer. """
@@ -64,6 +63,7 @@ def test_InceptionTime_starts_with_batchnorm(self):
 
         assert 'BatchNormalization' in str(type(model.layers[1])), 'Wrong layer type.'
 
+
     def test_InceptionTime_first_inception_module(self):
         """ Test layers of first inception module. """
         model_type = InceptionTime((None, 20, 3), 2)
@@ -73,8 +73,9 @@ def test_InceptionTime_first_inception_module(self):
         assert 'MaxPooling1D' in str(type(model.layers[3])), 'Wrong layer type.'
         assert 'Concatenate' in str(type(model.layers[8])), 'Wrong layer type.'
 
+
     def test_InceptionTime_depth(self):
-        """ ResNet model should have depth (number of residual modules) as defined by user. """
+        """ InceptionTime model should have depth (number of residual modules) as defined by user. """
         depths = 3
 
         model_type = InceptionTime((None, 20, 3), 2)
@@ -83,6 +84,7 @@ def test_InceptionTime_depth(self):
         concat_layers = [str(type(l)) for l in model.layers if 'concatenate' in str(type(l)).lower()]
         assert len(concat_layers) == depths, 'Wrong number of inception modules (network depths).'
 
+
     def test_InceptionTime_first_module_dim(self):
         """"The output shape throughout the first residual module should be (None, nr_timesteps, min_filters_number)"""
         min_filters_number = 16
@@ -95,8 +97,9 @@ def test_InceptionTime_first_module_dim(self):
         assert secondConvlayer.output_shape == (None, 30, min_filters_number)
         assert firstConcatlayer.output_shape == (None, 30, min_filters_number * 4)
 
+
     def test_InceptionTime_metrics(self):
-        """ResNet model should be compiled with the metrics that we give it"""
+        """InceptionTime model should be compiled with the metrics that we give it"""
         metrics = ['accuracy', 'mae']
         x_shape = (None, 20, 3)
         nr_classes = 2
@@ -110,6 +113,7 @@ def test_InceptionTime_metrics(self):
         for metric in metrics:
             assert metric in model_metrics
 
+
     def test_InceptionTime_hyperparameters(self):
         """ Network depth from range [5,5] should be 5.
         Maximum kernal size from range [12, 12] should be 12.
@@ -134,6 +138,7 @@ def test_InceptionTime_hyperparameters(self):
         assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
         assert hyperparams.get('filters_number') == 32, 'Wrong filter number'
 
+
     # Tests for general mcfly functionality:
     def test_generate_models_metrics(self):
         """ Test if correct number of models is generated and if metrics is correct. """

From a3084a7906e0940788abba600610d5fe2052cd5a Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 17:42:34 +0200
Subject: [PATCH 05/17] Moved out InceptionTime tests

---
 tests/test_modelgen.py                    | 85 ---------------------
 tests/tests_models/test_inception_time.py | 93 +++++++++++++++++++++++
 2 files changed, 93 insertions(+), 85 deletions(-)
 create mode 100755 tests/tests_models/test_inception_time.py

diff --git a/tests/test_modelgen.py b/tests/test_modelgen.py
index c3c0633..3fe6960 100644
--- a/tests/test_modelgen.py
+++ b/tests/test_modelgen.py
@@ -54,91 +54,6 @@ def generate_train_data(x_shape, nr_classes):
 class ModelGenerationSuite(unittest.TestCase):
     """Basic test cases."""
 
-
-    # Tests for InceptionTime model:
-    def test_InceptionTime_starts_with_batchnorm(self):
-        """ InceptionTime models should always start with a batch normalization layer. """
-        model_type = InceptionTime((None, 20, 3), 2)
-        model = model_type.create_model(16)
-
-        assert 'BatchNormalization' in str(type(model.layers[1])), 'Wrong layer type.'
-
-
-    def test_InceptionTime_first_inception_module(self):
-        """ Test layers of first inception module. """
-        model_type = InceptionTime((None, 20, 3), 2)
-        model = model_type.create_model(16)
-
-        assert 'Conv1D' or 'Convolution1D' in str(type(model.layers[2])), 'Wrong layer type.'
-        assert 'MaxPooling1D' in str(type(model.layers[3])), 'Wrong layer type.'
-        assert 'Concatenate' in str(type(model.layers[8])), 'Wrong layer type.'
-
-
-    def test_InceptionTime_depth(self):
-        """ InceptionTime model should have depth (number of residual modules) as defined by user. """
-        depths = 3
-
-        model_type = InceptionTime((None, 20, 3), 2)
-        model = model_type.create_model(16, network_depth=depths)
-
-        concat_layers = [str(type(l)) for l in model.layers if 'concatenate' in str(type(l)).lower()]
-        assert len(concat_layers) == depths, 'Wrong number of inception modules (network depths).'
-
-
-    def test_InceptionTime_first_module_dim(self):
-        """"The output shape throughout the first residual module should be (None, nr_timesteps, min_filters_number)"""
-        min_filters_number = 16
-
-        model_type = InceptionTime((None, 30, 5), 2)
-        model = model_type.create_model(min_filters_number)
-
-        secondConvlayer = model.layers[5]
-        firstConcatlayer = model.layers[8]
-        assert secondConvlayer.output_shape == (None, 30, min_filters_number)
-        assert firstConcatlayer.output_shape == (None, 30, min_filters_number * 4)
-
-
-    def test_InceptionTime_metrics(self):
-        """InceptionTime model should be compiled with the metrics that we give it"""
-        metrics = ['accuracy', 'mae']
-        x_shape = (None, 20, 3)
-        nr_classes = 2
-        X_train, y_train = generate_train_data(x_shape, nr_classes)
-
-        model_type = InceptionTime(x_shape, nr_classes, metrics=metrics)
-        model = model_type.create_model(16)
-        model.fit(X_train, y_train)
-
-        model_metrics = [m.name for m in model.metrics]
-        for metric in metrics:
-            assert metric in model_metrics
-
-
-    def test_InceptionTime_hyperparameters(self):
-        """ Network depth from range [5,5] should be 5.
-        Maximum kernal size from range [12, 12] should be 12.
-        Minimum filter number from range [32, 32] should be 32.  """
-        custom_settings = get_default()
-        x_shape = (None, 20, 3)
-        kwargs = {'IT_min_network_depth': 5,
-                  'IT_max_network_depth': 5,
-                  'IT_min_max_kernel_size': 10,
-                  'IT_max_max_kernel_size': 10,
-                  'IT_min_filters_number': 32,
-                  'IT_max_filters_number': 32}
-        # Replace default parameters with input
-        for key, value in kwargs.items():
-            if key in custom_settings:
-                custom_settings[key] = value
-
-        model_type = InceptionTime(x_shape, None, **custom_settings)
-        hyperparams = model_type.generate_hyperparameters()
-
-        assert hyperparams.get('network_depth') == 5, 'Wrong network depth'
-        assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
-        assert hyperparams.get('filters_number') == 32, 'Wrong filter number'
-
-
     # Tests for general mcfly functionality:
     def test_generate_models_metrics(self):
         """ Test if correct number of models is generated and if metrics is correct. """
diff --git a/tests/tests_models/test_inception_time.py b/tests/tests_models/test_inception_time.py
new file mode 100755
index 0000000..3137c79
--- /dev/null
+++ b/tests/tests_models/test_inception_time.py
@@ -0,0 +1,93 @@
+# -*- coding: utf-8 -*-
+
+import unittest
+from mcfly.models import InceptionTime
+from tests.test_modelgen import get_default, generate_train_data
+
+class InceptionTimeSuite(unittest.TestCase):
+    """
+    Test cases for InceptionTime models.
+    """
+
+    # Tests for InceptionTime model:
+    def test_InceptionTime_starts_with_batchnorm(self):
+        """ InceptionTime models should always start with a batch normalization layer. """
+        model_type = InceptionTime((None, 20, 3), 2)
+        model = model_type.create_model(16)
+
+        assert 'BatchNormalization' in str(type(model.layers[1])), 'Wrong layer type.'
+
+
+    def test_InceptionTime_first_inception_module(self):
+        """ Test layers of first inception module. """
+        model_type = InceptionTime((None, 20, 3), 2)
+        model = model_type.create_model(16)
+
+        assert 'Conv1D' or 'Convolution1D' in str(type(model.layers[2])), 'Wrong layer type.'
+        assert 'MaxPooling1D' in str(type(model.layers[3])), 'Wrong layer type.'
+        assert 'Concatenate' in str(type(model.layers[8])), 'Wrong layer type.'
+
+
+    def test_InceptionTime_depth(self):
+        """ InceptionTime model should have depth (number of residual modules) as defined by user. """
+        depths = 3
+
+        model_type = InceptionTime((None, 20, 3), 2)
+        model = model_type.create_model(16, network_depth=depths)
+
+        concat_layers = [str(type(l)) for l in model.layers if 'concatenate' in str(type(l)).lower()]
+        assert len(concat_layers) == depths, 'Wrong number of inception modules (network depths).'
+
+
+    def test_InceptionTime_first_module_dim(self):
+        """"The output shape throughout the first residual module should be (None, nr_timesteps, min_filters_number)"""
+        min_filters_number = 16
+
+        model_type = InceptionTime((None, 30, 5), 2)
+        model = model_type.create_model(min_filters_number)
+
+        secondConvlayer = model.layers[5]
+        firstConcatlayer = model.layers[8]
+        assert secondConvlayer.output_shape == (None, 30, min_filters_number)
+        assert firstConcatlayer.output_shape == (None, 30, min_filters_number * 4)
+
+
+    def test_InceptionTime_metrics(self):
+        """InceptionTime model should be compiled with the metrics that we give it"""
+        metrics = ['accuracy', 'mae']
+        x_shape = (None, 20, 3)
+        nr_classes = 2
+        X_train, y_train = generate_train_data(x_shape, nr_classes)
+
+        model_type = InceptionTime(x_shape, nr_classes, metrics=metrics)
+        model = model_type.create_model(16)
+        model.fit(X_train, y_train)
+
+        model_metrics = [m.name for m in model.metrics]
+        for metric in metrics:
+            assert metric in model_metrics
+
+
+    def test_InceptionTime_hyperparameters(self):
+        """ Network depth from range [5,5] should be 5.
+        Maximum kernal size from range [12, 12] should be 12.
+        Minimum filter number from range [32, 32] should be 32.  """
+        custom_settings = get_default()
+        x_shape = (None, 20, 3)
+        kwargs = {'IT_min_network_depth': 5,
+                  'IT_max_network_depth': 5,
+                  'IT_min_max_kernel_size': 10,
+                  'IT_max_max_kernel_size': 10,
+                  'IT_min_filters_number': 32,
+                  'IT_max_filters_number': 32}
+        # Replace default parameters with input
+        for key, value in kwargs.items():
+            if key in custom_settings:
+                custom_settings[key] = value
+
+        model_type = InceptionTime(x_shape, None, **custom_settings)
+        hyperparams = model_type.generate_hyperparameters()
+
+        assert hyperparams.get('network_depth') == 5, 'Wrong network depth'
+        assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
+        assert hyperparams.get('filters_number') == 32, 'Wrong filter number'

From 827713b62177c6a6768c009fcc5476756e1fbbd8 Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 17:44:07 +0200
Subject: [PATCH 06/17] Removed unused imports

---
 tests/test_modelgen.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_modelgen.py b/tests/test_modelgen.py
index 3fe6960..9285a1a 100644
--- a/tests/test_modelgen.py
+++ b/tests/test_modelgen.py
@@ -3,7 +3,7 @@
 import pytest
 from tensorflow.keras.models import Model
 from mcfly import modelgen
-from mcfly.models import CNN, ConvLSTM, ResNet, InceptionTime
+from mcfly.models import ResNet
 
 
 # TODO: Move this to an utils file, or obtain it from other source?

From e2ef96161f2ed9404c78a884a8ff6f35a5c0c13d Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 17:50:16 +0200
Subject: [PATCH 07/17] Test to guarantee that Travis is picking up these files

---
 tests/tests_models/test_cnn.py            | 4 ++++
 tests/tests_models/test_conv_lstm.py      | 6 ++++++
 tests/tests_models/test_inception_time.py | 6 ++++++
 tests/tests_models/test_resnet.py         | 6 ++++++
 4 files changed, 22 insertions(+)

diff --git a/tests/tests_models/test_cnn.py b/tests/tests_models/test_cnn.py
index 2897d26..f10df6d 100755
--- a/tests/tests_models/test_cnn.py
+++ b/tests/tests_models/test_cnn.py
@@ -96,3 +96,7 @@ def test_CNN_hyperparameters_fcnodes(self):
         hyperparams = model_type.generate_hyperparameters()
 
         assert hyperparams.get('fc_hidden_nodes') == 123
+
+    def test_cnn_travis(self):
+        """Not sure if Travis's pytest is picking up these files"""
+        assert False
diff --git a/tests/tests_models/test_conv_lstm.py b/tests/tests_models/test_conv_lstm.py
index 9e9fa2f..ae1a952 100755
--- a/tests/tests_models/test_conv_lstm.py
+++ b/tests/tests_models/test_conv_lstm.py
@@ -49,3 +49,9 @@ def test_deepconvlstm_starts_with_batchnorm(self):
                                            "lstm_dims": [32, 32]})
 
         assert 'BatchNormalization' in str(type(model.layers[0])), 'Wrong layer type.'
+
+
+
+    def test_deepconvlstm_travis(self):
+        """Not sure if Travis's pytest is picking up these files"""
+        assert False
diff --git a/tests/tests_models/test_inception_time.py b/tests/tests_models/test_inception_time.py
index 3137c79..0f9bd0c 100755
--- a/tests/tests_models/test_inception_time.py
+++ b/tests/tests_models/test_inception_time.py
@@ -91,3 +91,9 @@ def test_InceptionTime_hyperparameters(self):
         assert hyperparams.get('network_depth') == 5, 'Wrong network depth'
         assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
         assert hyperparams.get('filters_number') == 32, 'Wrong filter number'
+
+
+    def test_InceptionTime_travis(self):
+        """Not sure if Travis's pytest is picking up these files"""
+        assert False
+
diff --git a/tests/tests_models/test_resnet.py b/tests/tests_models/test_resnet.py
index 31ebe37..d377517 100755
--- a/tests/tests_models/test_resnet.py
+++ b/tests/tests_models/test_resnet.py
@@ -87,3 +87,9 @@ def test_ResNet_hyperparameters(self):
         assert hyperparams.get('network_depth') == 4, 'Wrong network depth'
         assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
         assert hyperparams.get('min_filters_number') == 16, 'Wrong filter number'
+
+
+    def test_ResNet_travis(self):
+        """Not sure if Travis's pytest is picking up these files"""
+        assert False
+

From ae99b38e1558a88b664dd0010df005cd9152d54d Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 17:57:01 +0200
Subject: [PATCH 08/17] Revert "Test to guarantee that Travis is picking up
 these files"

This reverts commit e2ef96161f2ed9404c78a884a8ff6f35a5c0c13d.

Previous commit proved unnecessary.
---
 tests/tests_models/test_cnn.py            | 4 ----
 tests/tests_models/test_conv_lstm.py      | 6 ------
 tests/tests_models/test_inception_time.py | 6 ------
 tests/tests_models/test_resnet.py         | 6 ------
 4 files changed, 22 deletions(-)

diff --git a/tests/tests_models/test_cnn.py b/tests/tests_models/test_cnn.py
index f10df6d..2897d26 100755
--- a/tests/tests_models/test_cnn.py
+++ b/tests/tests_models/test_cnn.py
@@ -96,7 +96,3 @@ def test_CNN_hyperparameters_fcnodes(self):
         hyperparams = model_type.generate_hyperparameters()
 
         assert hyperparams.get('fc_hidden_nodes') == 123
-
-    def test_cnn_travis(self):
-        """Not sure if Travis's pytest is picking up these files"""
-        assert False
diff --git a/tests/tests_models/test_conv_lstm.py b/tests/tests_models/test_conv_lstm.py
index ae1a952..9e9fa2f 100755
--- a/tests/tests_models/test_conv_lstm.py
+++ b/tests/tests_models/test_conv_lstm.py
@@ -49,9 +49,3 @@ def test_deepconvlstm_starts_with_batchnorm(self):
                                            "lstm_dims": [32, 32]})
 
         assert 'BatchNormalization' in str(type(model.layers[0])), 'Wrong layer type.'
-
-
-
-    def test_deepconvlstm_travis(self):
-        """Not sure if Travis's pytest is picking up these files"""
-        assert False
diff --git a/tests/tests_models/test_inception_time.py b/tests/tests_models/test_inception_time.py
index 0f9bd0c..3137c79 100755
--- a/tests/tests_models/test_inception_time.py
+++ b/tests/tests_models/test_inception_time.py
@@ -91,9 +91,3 @@ def test_InceptionTime_hyperparameters(self):
         assert hyperparams.get('network_depth') == 5, 'Wrong network depth'
         assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
         assert hyperparams.get('filters_number') == 32, 'Wrong filter number'
-
-
-    def test_InceptionTime_travis(self):
-        """Not sure if Travis's pytest is picking up these files"""
-        assert False
-
diff --git a/tests/tests_models/test_resnet.py b/tests/tests_models/test_resnet.py
index d377517..31ebe37 100755
--- a/tests/tests_models/test_resnet.py
+++ b/tests/tests_models/test_resnet.py
@@ -87,9 +87,3 @@ def test_ResNet_hyperparameters(self):
         assert hyperparams.get('network_depth') == 4, 'Wrong network depth'
         assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
         assert hyperparams.get('min_filters_number') == 16, 'Wrong filter number'
-
-
-    def test_ResNet_travis(self):
-        """Not sure if Travis's pytest is picking up these files"""
-        assert False
-

From 35818a9a5b1baf55ccf39c0b584b9216ba2dbf89 Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 17:59:55 +0200
Subject: [PATCH 09/17] Moved files out of subfolder because they failed
 imports

---
 ...arameter_generator.py => test_base_hyperparameter_generator.py | 0
 tests/test_find_architecture.py => test_find_architecture.py      | 0
 tests/test_integration.py => test_integration.py                  | 0
 tests/test_modelgen.py => test_modelgen.py                        | 0
 tests/test_storage.py => test_storage.py                          | 0
 tests/test_tools.py => test_tools.py                              | 0
 {tests/tests_models => tests_models}/test_cnn.py                  | 0
 {tests/tests_models => tests_models}/test_conv_lstm.py            | 0
 {tests/tests_models => tests_models}/test_inception_time.py       | 0
 {tests/tests_models => tests_models}/test_resnet.py               | 0
 10 files changed, 0 insertions(+), 0 deletions(-)
 rename tests/test_base_hyperparameter_generator.py => test_base_hyperparameter_generator.py (100%)
 rename tests/test_find_architecture.py => test_find_architecture.py (100%)
 rename tests/test_integration.py => test_integration.py (100%)
 rename tests/test_modelgen.py => test_modelgen.py (100%)
 rename tests/test_storage.py => test_storage.py (100%)
 rename tests/test_tools.py => test_tools.py (100%)
 rename {tests/tests_models => tests_models}/test_cnn.py (100%)
 rename {tests/tests_models => tests_models}/test_conv_lstm.py (100%)
 rename {tests/tests_models => tests_models}/test_inception_time.py (100%)
 rename {tests/tests_models => tests_models}/test_resnet.py (100%)

diff --git a/tests/test_base_hyperparameter_generator.py b/test_base_hyperparameter_generator.py
similarity index 100%
rename from tests/test_base_hyperparameter_generator.py
rename to test_base_hyperparameter_generator.py
diff --git a/tests/test_find_architecture.py b/test_find_architecture.py
similarity index 100%
rename from tests/test_find_architecture.py
rename to test_find_architecture.py
diff --git a/tests/test_integration.py b/test_integration.py
similarity index 100%
rename from tests/test_integration.py
rename to test_integration.py
diff --git a/tests/test_modelgen.py b/test_modelgen.py
similarity index 100%
rename from tests/test_modelgen.py
rename to test_modelgen.py
diff --git a/tests/test_storage.py b/test_storage.py
similarity index 100%
rename from tests/test_storage.py
rename to test_storage.py
diff --git a/tests/test_tools.py b/test_tools.py
similarity index 100%
rename from tests/test_tools.py
rename to test_tools.py
diff --git a/tests/tests_models/test_cnn.py b/tests_models/test_cnn.py
similarity index 100%
rename from tests/tests_models/test_cnn.py
rename to tests_models/test_cnn.py
diff --git a/tests/tests_models/test_conv_lstm.py b/tests_models/test_conv_lstm.py
similarity index 100%
rename from tests/tests_models/test_conv_lstm.py
rename to tests_models/test_conv_lstm.py
diff --git a/tests/tests_models/test_inception_time.py b/tests_models/test_inception_time.py
similarity index 100%
rename from tests/tests_models/test_inception_time.py
rename to tests_models/test_inception_time.py
diff --git a/tests/tests_models/test_resnet.py b/tests_models/test_resnet.py
similarity index 100%
rename from tests/tests_models/test_resnet.py
rename to tests_models/test_resnet.py

From 2983d2a6675d31e5edccd6c2b0807ae9137d9089 Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 18:04:34 +0200
Subject: [PATCH 10/17] Revert "Moved files out of subfolder because they
 failed imports"

This reverts commit 35818a9a5b1baf55ccf39c0b584b9216ba2dbf89.

Moved wrong files
---
 .../test_base_hyperparameter_generator.py                         | 0
 test_find_architecture.py => tests/test_find_architecture.py      | 0
 test_integration.py => tests/test_integration.py                  | 0
 test_modelgen.py => tests/test_modelgen.py                        | 0
 test_storage.py => tests/test_storage.py                          | 0
 test_tools.py => tests/test_tools.py                              | 0
 {tests_models => tests/tests_models}/test_cnn.py                  | 0
 {tests_models => tests/tests_models}/test_conv_lstm.py            | 0
 {tests_models => tests/tests_models}/test_inception_time.py       | 0
 {tests_models => tests/tests_models}/test_resnet.py               | 0
 10 files changed, 0 insertions(+), 0 deletions(-)
 rename test_base_hyperparameter_generator.py => tests/test_base_hyperparameter_generator.py (100%)
 rename test_find_architecture.py => tests/test_find_architecture.py (100%)
 rename test_integration.py => tests/test_integration.py (100%)
 rename test_modelgen.py => tests/test_modelgen.py (100%)
 rename test_storage.py => tests/test_storage.py (100%)
 rename test_tools.py => tests/test_tools.py (100%)
 rename {tests_models => tests/tests_models}/test_cnn.py (100%)
 rename {tests_models => tests/tests_models}/test_conv_lstm.py (100%)
 rename {tests_models => tests/tests_models}/test_inception_time.py (100%)
 rename {tests_models => tests/tests_models}/test_resnet.py (100%)

diff --git a/test_base_hyperparameter_generator.py b/tests/test_base_hyperparameter_generator.py
similarity index 100%
rename from test_base_hyperparameter_generator.py
rename to tests/test_base_hyperparameter_generator.py
diff --git a/test_find_architecture.py b/tests/test_find_architecture.py
similarity index 100%
rename from test_find_architecture.py
rename to tests/test_find_architecture.py
diff --git a/test_integration.py b/tests/test_integration.py
similarity index 100%
rename from test_integration.py
rename to tests/test_integration.py
diff --git a/test_modelgen.py b/tests/test_modelgen.py
similarity index 100%
rename from test_modelgen.py
rename to tests/test_modelgen.py
diff --git a/test_storage.py b/tests/test_storage.py
similarity index 100%
rename from test_storage.py
rename to tests/test_storage.py
diff --git a/test_tools.py b/tests/test_tools.py
similarity index 100%
rename from test_tools.py
rename to tests/test_tools.py
diff --git a/tests_models/test_cnn.py b/tests/tests_models/test_cnn.py
similarity index 100%
rename from tests_models/test_cnn.py
rename to tests/tests_models/test_cnn.py
diff --git a/tests_models/test_conv_lstm.py b/tests/tests_models/test_conv_lstm.py
similarity index 100%
rename from tests_models/test_conv_lstm.py
rename to tests/tests_models/test_conv_lstm.py
diff --git a/tests_models/test_inception_time.py b/tests/tests_models/test_inception_time.py
similarity index 100%
rename from tests_models/test_inception_time.py
rename to tests/tests_models/test_inception_time.py
diff --git a/tests_models/test_resnet.py b/tests/tests_models/test_resnet.py
similarity index 100%
rename from tests_models/test_resnet.py
rename to tests/tests_models/test_resnet.py

From 3828386a400ca009d5ec1f94421f7c3f66cb319a Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 18:06:35 +0200
Subject: [PATCH 11/17] Moved model tests out from subfolder because of failed
 imports in Travis

---
 tests/{tests_models => }/test_cnn.py            | 0
 tests/{tests_models => }/test_conv_lstm.py      | 0
 tests/{tests_models => }/test_inception_time.py | 0
 tests/{tests_models => }/test_resnet.py         | 0
 4 files changed, 0 insertions(+), 0 deletions(-)
 rename tests/{tests_models => }/test_cnn.py (100%)
 rename tests/{tests_models => }/test_conv_lstm.py (100%)
 rename tests/{tests_models => }/test_inception_time.py (100%)
 rename tests/{tests_models => }/test_resnet.py (100%)

diff --git a/tests/tests_models/test_cnn.py b/tests/test_cnn.py
similarity index 100%
rename from tests/tests_models/test_cnn.py
rename to tests/test_cnn.py
diff --git a/tests/tests_models/test_conv_lstm.py b/tests/test_conv_lstm.py
similarity index 100%
rename from tests/tests_models/test_conv_lstm.py
rename to tests/test_conv_lstm.py
diff --git a/tests/tests_models/test_inception_time.py b/tests/test_inception_time.py
similarity index 100%
rename from tests/tests_models/test_inception_time.py
rename to tests/test_inception_time.py
diff --git a/tests/tests_models/test_resnet.py b/tests/test_resnet.py
similarity index 100%
rename from tests/tests_models/test_resnet.py
rename to tests/test_resnet.py

From 24284ecc2db6dce48c834f66d8f734bb5a1e78aa Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 18:07:22 +0200
Subject: [PATCH 12/17] Corrected import

---
 tests/test_cnn.py            | 2 +-
 tests/test_conv_lstm.py      | 2 +-
 tests/test_inception_time.py | 2 +-
 tests/test_resnet.py         | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/test_cnn.py b/tests/test_cnn.py
index 2897d26..d20f59d 100755
--- a/tests/test_cnn.py
+++ b/tests/test_cnn.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 import unittest
 from mcfly.models import CNN
-from tests.test_modelgen import get_default
+from test_modelgen import get_default
 
 
 class CNNSuite(unittest.TestCase):
diff --git a/tests/test_conv_lstm.py b/tests/test_conv_lstm.py
index 9e9fa2f..9d0ba93 100755
--- a/tests/test_conv_lstm.py
+++ b/tests/test_conv_lstm.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 import unittest
 from mcfly.models import ConvLSTM
-from tests.test_modelgen import get_default
+from test_modelgen import get_default
 
 class ConvLSTMSuite(unittest.TestCase):
     """
diff --git a/tests/test_inception_time.py b/tests/test_inception_time.py
index 3137c79..a7b28b3 100755
--- a/tests/test_inception_time.py
+++ b/tests/test_inception_time.py
@@ -2,7 +2,7 @@
 
 import unittest
 from mcfly.models import InceptionTime
-from tests.test_modelgen import get_default, generate_train_data
+from test_modelgen import get_default, generate_train_data
 
 class InceptionTimeSuite(unittest.TestCase):
     """
diff --git a/tests/test_resnet.py b/tests/test_resnet.py
index 31ebe37..044b41c 100755
--- a/tests/test_resnet.py
+++ b/tests/test_resnet.py
@@ -2,7 +2,7 @@
 
 import unittest
 from mcfly.models import ResNet
-from tests.test_modelgen import get_default, generate_train_data
+from test_modelgen import get_default, generate_train_data
 
 class ResNetSuite(unittest.TestCase):
     """

From e1bf3f142e0aa8bf0e1e9dcda7b5efc77a793ec6 Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 18:11:52 +0200
Subject: [PATCH 13/17] Added main to execute test locally

---
 tests/test_cnn.py            | 5 +++++
 tests/test_conv_lstm.py      | 4 ++++
 tests/test_inception_time.py | 4 ++++
 tests/test_resnet.py         | 4 ++++
 4 files changed, 17 insertions(+)

diff --git a/tests/test_cnn.py b/tests/test_cnn.py
index d20f59d..cb366e9 100755
--- a/tests/test_cnn.py
+++ b/tests/test_cnn.py
@@ -96,3 +96,8 @@ def test_CNN_hyperparameters_fcnodes(self):
         hyperparams = model_type.generate_hyperparameters()
 
         assert hyperparams.get('fc_hidden_nodes') == 123
+
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_conv_lstm.py b/tests/test_conv_lstm.py
index 9d0ba93..2e62f6d 100755
--- a/tests/test_conv_lstm.py
+++ b/tests/test_conv_lstm.py
@@ -49,3 +49,7 @@ def test_deepconvlstm_starts_with_batchnorm(self):
                                            "lstm_dims": [32, 32]})
 
         assert 'BatchNormalization' in str(type(model.layers[0])), 'Wrong layer type.'
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_inception_time.py b/tests/test_inception_time.py
index a7b28b3..62d8d4a 100755
--- a/tests/test_inception_time.py
+++ b/tests/test_inception_time.py
@@ -91,3 +91,7 @@ def test_InceptionTime_hyperparameters(self):
         assert hyperparams.get('network_depth') == 5, 'Wrong network depth'
         assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
         assert hyperparams.get('filters_number') == 32, 'Wrong filter number'
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_resnet.py b/tests/test_resnet.py
index 044b41c..290c5dc 100755
--- a/tests/test_resnet.py
+++ b/tests/test_resnet.py
@@ -87,3 +87,7 @@ def test_ResNet_hyperparameters(self):
         assert hyperparams.get('network_depth') == 4, 'Wrong network depth'
         assert hyperparams.get('max_kernel_size') == 10, 'Wrong kernel'
         assert hyperparams.get('min_filters_number') == 16, 'Wrong filter number'
+
+
+if __name__ == '__main__':
+    unittest.main()

From 07cf115cad793259c299dd5a8f39e8b3ea036953 Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 18:12:45 +0200
Subject: [PATCH 14/17] Import generate train data

---
 tests/test_cnn.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_cnn.py b/tests/test_cnn.py
index cb366e9..3506d05 100755
--- a/tests/test_cnn.py
+++ b/tests/test_cnn.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 import unittest
 from mcfly.models import CNN
-from test_modelgen import get_default
+from test_modelgen import get_default, generate_train_data
 
 
 class CNNSuite(unittest.TestCase):
@@ -54,7 +54,7 @@ def test_cnn_metrics(self):
         metrics = ['accuracy', 'mae']
         x_shape = (None, 20, 3)
         nr_classes = 2
-        X_train, y_train = self._generate_train_data(x_shape, nr_classes)
+        X_train, y_train = generate_train_data(x_shape, nr_classes)
 
         model_type = CNN(x_shape, nr_classes, metrics=metrics)
         model = model_type.create_model(**{"filters": [32, 32],

From 4823927d13fdd904efe37f0260a78a648466254c Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 18:28:42 +0200
Subject: [PATCH 15/17] Fixed typo

---
 tests/test_resnet.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_resnet.py b/tests/test_resnet.py
index 290c5dc..f654416 100755
--- a/tests/test_resnet.py
+++ b/tests/test_resnet.py
@@ -71,7 +71,7 @@ def test_ResNet_hyperparameters(self):
         Minimum filter number from range [16, 16] should be 16.  """
         custom_settings = get_default()
         kwargs = {'resnet_min_network_depth': 4,
-                  'resnet_mmax_network_depth': 4,
+                  'resnet_max_network_depth': 4,
                   'resnet_min_max_kernel_size': 10,
                   'resnet_max_max_kernel_size': 10,
                   'resnet_min_filters_number': 16,

From d772162d354033e605a85efca6e231d9ada152e2 Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Thu, 30 Jul 2020 18:36:30 +0200
Subject: [PATCH 16/17] Fixed extra tab

---
 tests/test_modelgen.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/test_modelgen.py b/tests/test_modelgen.py
index 9285a1a..a0563a2 100644
--- a/tests/test_modelgen.py
+++ b/tests/test_modelgen.py
@@ -46,9 +46,9 @@ def get_default():
 
 # TODO: Move this to an utils file?
 def generate_train_data(x_shape, nr_classes):
-        X_train = np.random.rand(1, *x_shape[1:])
-        y_train = np.random.randint(0, 1, size=(1, nr_classes))
-        return X_train, y_train
+    X_train = np.random.rand(1, *x_shape[1:])
+    y_train = np.random.randint(0, 1, size=(1, nr_classes))
+    return X_train, y_train
 
 
 class ModelGenerationSuite(unittest.TestCase):

From 5ae59002b2f07aa2c78fca50fcca2c8c0798fcfd Mon Sep 17 00:00:00 2001
From: breixo <breixo.solino@gmail.com>
Date: Fri, 31 Jul 2020 11:38:18 +0200
Subject: [PATCH 17/17] Removed todos

---
 tests/test_modelgen.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tests/test_modelgen.py b/tests/test_modelgen.py
index a0563a2..31a3889 100644
--- a/tests/test_modelgen.py
+++ b/tests/test_modelgen.py
@@ -6,7 +6,6 @@
 from mcfly.models import ResNet
 
 
-# TODO: Move this to an utils file, or obtain it from other source?
 def get_default():
     """ "Define mcflu default parameters as dictionary. """
     settings = {'metrics': ['accuracy'],
@@ -44,7 +43,6 @@ def get_default():
     return settings
 
 
-# TODO: Move this to an utils file?
 def generate_train_data(x_shape, nr_classes):
     X_train = np.random.rand(1, *x_shape[1:])
     y_train = np.random.randint(0, 1, size=(1, nr_classes))