diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_conv_skip_connection.py b/tests/test_conv_skip_connection.py
new file mode 100644
index 0000000..e248b7c
--- /dev/null
+++ b/tests/test_conv_skip_connection.py
@@ -0,0 +1,16 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.ConvSkipConnection(32, batch_normalization=True, dropout=0.2)(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_convolution_2d.py b/tests/test_convolution_2d.py
new file mode 100644
index 0000000..501d38e
--- /dev/null
+++ b/tests/test_convolution_2d.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.Convolution2D(
+        padding="same", activation="relu", batch_normalization=True, dropout=0.2
+    )(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_dense_net_convolution_block.py b/tests/test_dense_net_convolution_block.py
new file mode 100644
index 0000000..599862e
--- /dev/null
+++ b/tests/test_dense_net_convolution_block.py
@@ -0,0 +1,20 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.Conv2D(
+        3,
+        1,
+    )(inputs)
+    x = modules.DenseNetConvolutionBlock(growth_rate=8, use_bias=True)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_dense_net_transition_block.py b/tests/test_dense_net_transition_block.py
new file mode 100644
index 0000000..65908ea
--- /dev/null
+++ b/tests/test_dense_net_transition_block.py
@@ -0,0 +1,21 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.Conv2D(
+        3,
+        1,
+    )(inputs)
+    x = modules.DenseNetConvolutionBlock(growth_rate=32, use_bias=True)(x)
+    x = modules.DenseNetTransitionBlock(reduction=0.125)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_dense_skip_connection.py b/tests/test_dense_skip_connection.py
new file mode 100644
index 0000000..65605b6
--- /dev/null
+++ b/tests/test_dense_skip_connection.py
@@ -0,0 +1,17 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(13,))
+    x = modules.DenseSkipConnection(
+        32, activation="relu", batch_normalization=True, dropout=0.2
+    )(inputs)
+    outputs = keras.layers.Dense(1)(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_densely_connected.py b/tests/test_densely_connected.py
new file mode 100644
index 0000000..95d6077
--- /dev/null
+++ b/tests/test_densely_connected.py
@@ -0,0 +1,20 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(13,))
+    x = modules.DenselyConnected(
+        32, activation="relu", batch_normalization=True, dropout=0.2
+    )(inputs)
+    x = modules.DenselyConnected(
+        8, activation="relu", batch_normalization=True, dropout=0.2
+    )(x)
+    outputs = keras.layers.Dense(1)(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_densely_connected_convolutional_network121.py b/tests/test_densely_connected_convolutional_network121.py
new file mode 100644
index 0000000..1b179cf
--- /dev/null
+++ b/tests/test_densely_connected_convolutional_network121.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(32)(
+        inputs
+    )  # padding to increase dimenstions to 92x92
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.DenselyConnectedConvolutionalNetwork121()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_densely_connected_convolutional_network169.py b/tests/test_densely_connected_convolutional_network169.py
new file mode 100644
index 0000000..f405d13
--- /dev/null
+++ b/tests/test_densely_connected_convolutional_network169.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(32)(
+        inputs
+    )  # padding to increase dimenstions to 92x92
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.DenselyConnectedConvolutionalNetwork169()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_densely_connected_convolutional_network201.py b/tests/test_densely_connected_convolutional_network201.py
new file mode 100644
index 0000000..90bd22e
--- /dev/null
+++ b/tests/test_densely_connected_convolutional_network201.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(32)(
+        inputs
+    )  # padding to increase dimenstions to 92x92
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.DenselyConnectedConvolutionalNetwork201()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_densely_connected_network.py b/tests/test_densely_connected_network.py
new file mode 100644
index 0000000..9622ddb
--- /dev/null
+++ b/tests/test_densely_connected_network.py
@@ -0,0 +1,17 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import densenets
+
+
+def test():
+    inputs = keras.Input(shape=(13,))
+    x = densenets.DenselyConnectedNetwork(
+        [32, 8], batch_normalization=True, dropout=0.2
+    )(inputs)
+    outputs = keras.layers.Dense(1)(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_densely_connected_resnet.py b/tests/test_densely_connected_resnet.py
new file mode 100644
index 0000000..b7ce424
--- /dev/null
+++ b/tests/test_densely_connected_resnet.py
@@ -0,0 +1,17 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import densenets
+
+
+def test():
+    inputs = keras.Input(shape=(13,))
+    x = densenets.DenselyConnectedResnet(
+        [32, 8], batch_normalization=True, dropout=0.2
+    )(inputs)
+    outputs = keras.layers.Dense(1)(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_efficient_net.py b/tests/test_efficient_net.py
new file mode 100644
index 0000000..fe0b48a
--- /dev/null
+++ b/tests/test_efficient_net.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.EfficientNet(1, 1, 32)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_efficient_net_b0.py b/tests/test_efficient_net_b0.py
new file mode 100644
index 0000000..26248d0
--- /dev/null
+++ b/tests/test_efficient_net_b0.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.EfficientNetB0()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_efficient_net_b1.py b/tests/test_efficient_net_b1.py
new file mode 100644
index 0000000..b3c9efa
--- /dev/null
+++ b/tests/test_efficient_net_b1.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.EfficientNetB1()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_efficient_net_b2.py b/tests/test_efficient_net_b2.py
new file mode 100644
index 0000000..044a6e4
--- /dev/null
+++ b/tests/test_efficient_net_b2.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.EfficientNetB2()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_efficient_net_b3.py b/tests/test_efficient_net_b3.py
new file mode 100644
index 0000000..20cab99
--- /dev/null
+++ b/tests/test_efficient_net_b3.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.EfficientNetB3()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_efficient_net_b4.py b/tests/test_efficient_net_b4.py
new file mode 100644
index 0000000..fafbb6b
--- /dev/null
+++ b/tests/test_efficient_net_b4.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.EfficientNetB4()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_efficient_net_b5.py b/tests/test_efficient_net_b5.py
new file mode 100644
index 0000000..0ddf01d
--- /dev/null
+++ b/tests/test_efficient_net_b5.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+    x = convnets.EfficientNetB5()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_efficient_net_b6.py b/tests/test_efficient_net_b6.py
new file mode 100644
index 0000000..def0f19
--- /dev/null
+++ b/tests/test_efficient_net_b6.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.EfficientNetB6()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_efficient_net_b7.py b/tests/test_efficient_net_b7.py
new file mode 100644
index 0000000..faf8544
--- /dev/null
+++ b/tests/test_efficient_net_b7.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.EfficientNetB7()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_efficient_net_block.py b/tests/test_efficient_net_block.py
new file mode 100644
index 0000000..3c65ab1
--- /dev/null
+++ b/tests/test_efficient_net_block.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = modules.EfficientNetBlock(filters_in=3)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_generalized_dense_nets.py b/tests/test_generalized_dense_nets.py
new file mode 100644
index 0000000..8f14dba
--- /dev/null
+++ b/tests/test_generalized_dense_nets.py
@@ -0,0 +1,16 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = convnets.GeneralizedDenseNets([2, 2], use_bias=True)(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_generalized_vgg.py b/tests/test_generalized_vgg.py
new file mode 100644
index 0000000..8db1f64
--- /dev/null
+++ b/tests/test_generalized_vgg.py
@@ -0,0 +1,36 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test_1():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = convnets.GeneralizedVGG(
+        conv_config=[(2, 32), (2, 64)],
+        dense_config=[28],
+        conv_batch_norm=True,
+        conv_dropout=0.2,
+        dense_batch_norm=True,
+        dense_dropout=0.2,
+    )(inputs)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
+
+
+def test_2():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = convnets.GeneralizedVGG(
+        conv_config=[(2, 32), (2, 64)],
+        dense_config=[],
+        conv_batch_norm=True,
+        conv_dropout=0.2,
+    )(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_generalized_xception.py b/tests/test_generalized_xception.py
new file mode 100644
index 0000000..9dd49f1
--- /dev/null
+++ b/tests/test_generalized_xception.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.GeneralizedXception(channel_coefficient=4, depth_coefficient=2)(x)
+    x = keras.layers.GlobalAveragePooling2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_inception_block.py b/tests/test_inception_block.py
new file mode 100644
index 0000000..a8a8f0b
--- /dev/null
+++ b/tests/test_inception_block.py
@@ -0,0 +1,25 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.InceptionBlock(
+        [
+            [(32, (1, 1), (1, 1), "same"), (32, (3, 3), (1, 1), "same")],
+            [(32, (1, 1), (1, 1), "same"), (32, (3, 1), (1, 1), "same")],
+            [(32, (1, 1), (1, 1), "same"), (32, (1, 3), (1, 1), "same")],
+        ],
+        keras.layers.AveragePooling2D(padding="same", strides=(1, 1)),
+        use_bias=True,
+        dropout=0.2,
+    )(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_inception_conv.py b/tests/test_inception_conv.py
new file mode 100644
index 0000000..b7b523b
--- /dev/null
+++ b/tests/test_inception_conv.py
@@ -0,0 +1,16 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.InceptionConv(32, (32, 32), dropout=0.2)(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_inception_res_net_block.py b/tests/test_inception_res_net_block.py
new file mode 100644
index 0000000..21c1f7c
--- /dev/null
+++ b/tests/test_inception_res_net_block.py
@@ -0,0 +1,34 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test_1():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.InceptionResNetBlock(1.0, "block8")(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
+
+
+def test_2():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.InceptionResNetBlock(1.0, "block17")(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
+
+
+def test_3():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.InceptionResNetBlock(1.0, "block35")(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_inception_res_net_conv_2d.py b/tests/test_inception_res_net_conv_2d.py
new file mode 100644
index 0000000..a6e31ac
--- /dev/null
+++ b/tests/test_inception_res_net_conv_2d.py
@@ -0,0 +1,16 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.InceptionResNetConv2D(32, 3)(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_inception_res_net_v2.py b/tests/test_inception_res_net_v2.py
new file mode 100644
index 0000000..cd83105
--- /dev/null
+++ b/tests/test_inception_res_net_v2.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(22)(
+        inputs
+    )  # padding to increase dimenstions to 72x72
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.InceptionResNetV2()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_inception_v3.py b/tests/test_inception_v3.py
new file mode 100644
index 0000000..129056f
--- /dev/null
+++ b/tests/test_inception_v3.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(25)(
+        inputs
+    )  # padding to increase dimenstions to 78x78
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.InceptionV3()(x)
+    x = keras.layers.GlobalAveragePooling2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_inverted_res_block.py b/tests/test_inverted_res_block.py
new file mode 100644
index 0000000..5fff3b0
--- /dev/null
+++ b/tests/test_inverted_res_block.py
@@ -0,0 +1,16 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.InvertedResBlock(filters=3, alpha=1.0, expansion=10)(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_mobile_net.py b/tests/test_mobile_net.py
new file mode 100644
index 0000000..e88175c
--- /dev/null
+++ b/tests/test_mobile_net.py
@@ -0,0 +1,37 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test_1():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(67)(
+        inputs
+    )  # padding to increase dimenstions to 162x162
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.MobileNet()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
+
+
+def test_2():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(67)(
+        inputs
+    )  # padding to increase dimenstions to 162x162
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.MobileNet([(32, 2), (32, 1), (64, 2), (64, 1)])(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_mobile_net_conv_block.py b/tests/test_mobile_net_conv_block.py
new file mode 100644
index 0000000..1bf529d
--- /dev/null
+++ b/tests/test_mobile_net_conv_block.py
@@ -0,0 +1,16 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.MobileNetConvBlock(filters=3, alpha=1.0)(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_mobile_net_depth_wise_conv_block.py b/tests/test_mobile_net_depth_wise_conv_block.py
new file mode 100644
index 0000000..e967872
--- /dev/null
+++ b/tests/test_mobile_net_depth_wise_conv_block.py
@@ -0,0 +1,16 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.MobileNetDepthWiseConvBlock(pointwise_conv_filters=32, alpha=1)(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_mobile_net_v2.py b/tests/test_mobile_net_v2.py
new file mode 100644
index 0000000..f172cc1
--- /dev/null
+++ b/tests/test_mobile_net_v2.py
@@ -0,0 +1,37 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test_1():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(13)(
+        inputs
+    )  # padding to increase dimenstions to 54x54
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.MobileNetV2()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
+
+
+def test_2():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(13)(
+        inputs
+    )  # padding to increase dimenstions to 54x54
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.MobileNetV2([(16, 1, 1), (24, 2, 6), (24, 2, 6)])(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_mobile_net_v3.py b/tests/test_mobile_net_v3.py
new file mode 100644
index 0000000..90bb464
--- /dev/null
+++ b/tests/test_mobile_net_v3.py
@@ -0,0 +1,35 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test_1():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.MobileNetV3(config="large")(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
+
+
+def test_2():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.MobileNetV3(config="small")(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
\ No newline at end of file
diff --git a/tests/test_nas_net.py b/tests/test_nas_net.py
new file mode 100644
index 0000000..5f87aa1
--- /dev/null
+++ b/tests/test_nas_net.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.NASNet(96, 1, 32)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_nas_net_large.py b/tests/test_nas_net_large.py
new file mode 100644
index 0000000..31dc7d1
--- /dev/null
+++ b/tests/test_nas_net_large.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.NASNetLarge()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_nas_net_mobile.py b/tests/test_nas_net_mobile.py
new file mode 100644
index 0000000..7b28af8
--- /dev/null
+++ b/tests/test_nas_net_mobile.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.NASNetMobile()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_nas_net_separable_conv_block.py b/tests/test_nas_net_separable_conv_block.py
new file mode 100644
index 0000000..ae040da
--- /dev/null
+++ b/tests/test_nas_net_separable_conv_block.py
@@ -0,0 +1,17 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.Convolution2D(padding="same")(inputs)
+    x = modules.NASNetSeparableConvBlock(32)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_res_net.py b/tests/test_res_net.py
new file mode 100644
index 0000000..2e79f32
--- /dev/null
+++ b/tests/test_res_net.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = convnets.ResNet([(32, 3), (64, 5), (32, 5), (64, 3)])(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_net101.py b/tests/test_res_net101.py
new file mode 100644
index 0000000..2eb4a9d
--- /dev/null
+++ b/tests/test_res_net101.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = convnets.ResNet101()(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_net101_v2.py b/tests/test_res_net101_v2.py
new file mode 100644
index 0000000..96a6ab8
--- /dev/null
+++ b/tests/test_res_net101_v2.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = convnets.ResNet101V2()(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_net152.py b/tests/test_res_net152.py
new file mode 100644
index 0000000..ed287fe
--- /dev/null
+++ b/tests/test_res_net152.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = convnets.ResNet152()(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_net152_v2.py b/tests/test_res_net152_v2.py
new file mode 100644
index 0000000..835b20f
--- /dev/null
+++ b/tests/test_res_net152_v2.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = convnets.ResNet152V2()(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_net50.py b/tests/test_res_net50.py
new file mode 100644
index 0000000..5f24b02
--- /dev/null
+++ b/tests/test_res_net50.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = convnets.ResNet50()(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_net50_v2.py b/tests/test_res_net50_v2.py
new file mode 100644
index 0000000..dd18559
--- /dev/null
+++ b/tests/test_res_net50_v2.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = convnets.ResNet50V2()(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_net_block.py b/tests/test_res_net_block.py
new file mode 100644
index 0000000..a47e404
--- /dev/null
+++ b/tests/test_res_net_block.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = modules.ResNetBlock(filters=32)(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_net_v2.py b/tests/test_res_net_v2.py
new file mode 100644
index 0000000..0c949e4
--- /dev/null
+++ b/tests/test_res_net_v2.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = convnets.ResNetV2([(32, 3), (64, 5), (32, 5), (64, 3)])(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_net_v2_block.py b/tests/test_res_net_v2_block.py
new file mode 100644
index 0000000..86e996f
--- /dev/null
+++ b/tests/test_res_net_v2_block.py
@@ -0,0 +1,18 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+inputs = keras.Input(shape=(28, 28, 1))
+x = keras.layers.ZeroPadding2D(2)(inputs)         # padding to increase dimenstions to 32x32
+x = keras.layers.Conv2D(3, 1, padding='same')(x)  # increasing the number of channels to 3
+x = modules.ResNetV2Block(filters=32)(x)
+x = keras.layers.GlobalAvgPool2D()(x)
+outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+model = keras.models.Model(inputs=inputs, outputs=outputs) 
+
diff --git a/tests/test_res_next.py b/tests/test_res_next.py
new file mode 100644
index 0000000..03de72f
--- /dev/null
+++ b/tests/test_res_next.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.ResNeXt([(32, 3), (64, 5), (32, 5), (64, 3)])(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_res_next101.py b/tests/test_res_next101.py
new file mode 100644
index 0000000..2ae39bd
--- /dev/null
+++ b/tests/test_res_next101.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.ResNeXt101()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_res_next152.py b/tests/test_res_next152.py
new file mode 100644
index 0000000..6cf4a53
--- /dev/null
+++ b/tests/test_res_next152.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.ResNeXt152()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_res_next50.py b/tests/test_res_next50.py
new file mode 100644
index 0000000..5cde6e9
--- /dev/null
+++ b/tests/test_res_next50.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.ResNeXt50()(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_res_next_block.py b/tests/test_res_next_block.py
new file mode 100644
index 0000000..852a735
--- /dev/null
+++ b/tests/test_res_next_block.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = modules.ResNeXtBlock(filters=32)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_rescale.py b/tests/test_rescale.py
new file mode 100644
index 0000000..5471035
--- /dev/null
+++ b/tests/test_rescale.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+
+    x_train = np.expand_dims(x_train, -1)
+    y_train = keras.utils.to_categorical(y_train, 10)
+
+    x_test = np.expand_dims(x_test, -1)
+    y_test = keras.utils.to_categorical(y_test, 10)
+
+    np.min(x_train), np.max(x_train)
+
+    x = modules.Rescale()(x_train)
+    assert (np.min(x), np.max(x)) == (0, 1)
diff --git a/tests/test_se_block.py b/tests/test_se_block.py
new file mode 100644
index 0000000..9ec5b61
--- /dev/null
+++ b/tests/test_se_block.py
@@ -0,0 +1,19 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.Conv2D(3, 1, padding="same")(
+        inputs
+    )  # increasing the number of channels to 3
+    x = modules.SEBlock(filters=3, se_ratio=32.0)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_vgg_16.py b/tests/test_vgg_16.py
new file mode 100644
index 0000000..de8618e
--- /dev/null
+++ b/tests/test_vgg_16.py
@@ -0,0 +1,36 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test_1():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.VGG16()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
+
+
+def test_2():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.VGG16(use_dense=False)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_vgg_19.py b/tests/test_vgg_19.py
new file mode 100644
index 0000000..faaa82f
--- /dev/null
+++ b/tests/test_vgg_19.py
@@ -0,0 +1,36 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test_1():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.VGG19()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
+
+
+def test_2():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.VGG19(use_dense=False)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_vgg_module.py b/tests/test_vgg_module.py
new file mode 100644
index 0000000..6cf5323
--- /dev/null
+++ b/tests/test_vgg_module.py
@@ -0,0 +1,16 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = modules.VGGModule(num_conv=3, num_filters=32, activation="relu")(inputs)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_xception_block.py b/tests/test_xception_block.py
new file mode 100644
index 0000000..8ddf382
--- /dev/null
+++ b/tests/test_xception_block.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import modules
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(32, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 32
+    x = modules.XceptionBlock(32)(x)
+    x = keras.layers.GlobalAvgPool2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)
diff --git a/tests/test_xception_net.py b/tests/test_xception_net.py
new file mode 100644
index 0000000..fc9f556
--- /dev/null
+++ b/tests/test_xception_net.py
@@ -0,0 +1,22 @@
+import sys, os
+
+sys.path.append(os.path.dirname(os.getcwd()))
+
+from tensorflow import keras
+import numpy as np
+from pyradox import convnets
+
+
+def test():
+    inputs = keras.Input(shape=(28, 28, 1))
+    x = keras.layers.ZeroPadding2D(2)(
+        inputs
+    )  # padding to increase dimenstions to 32x32
+    x = keras.layers.Conv2D(3, (1, 1), padding="same")(
+        x
+    )  # increasing the number of channels to 3
+    x = convnets.XceptionNet()(x)
+    x = keras.layers.GlobalAveragePooling2D()(x)
+    outputs = keras.layers.Dense(10, activation="softmax")(x)
+
+    model = keras.models.Model(inputs=inputs, outputs=outputs)