diff --git a/requirements.txt b/requirements.txt
index 753c6e7f74687102959923303c80a8090bba8907..4dd28a7df8a030029f5c662a3e9373ad0a142647 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
Keras==2.2.4
numpy==1.15.4
tensorflow==1.12.0
+pytest==5.2.1
\ No newline at end of file
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/test/__init__.py b/test/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/test/test_inception_model.py b/test/test_inception_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..a03e50c13dc0bf8f3edb01d11547871e44b8e1c6
--- /dev/null
+++ b/test/test_inception_model.py
@@ -0,0 +1,110 @@
+import pytest
+from src.inception_model import InceptionModelBase
+import keras
+import tensorflow as tf
+
+
+class TestInceptionModelBase:
+
+ @pytest.fixture
+ def base(self):
+ return InceptionModelBase()
+
+ @pytest.fixture
+ def input_x(self):
+ return keras.Input(shape=(32, 32, 3))
+
+ def test_init(self, base):
+ assert base.number_of_blocks == 0
+ assert base.part_of_block == 0
+ assert base.ord_base == 96
+
+ def test_block_part_name(self, base):
+ assert base.block_part_name() == chr(96)
+ base.part_of_block += 1
+ assert base.block_part_name() == 'a'
+
+ def test_create_conv_tower_3x3(self, base, input_x):
+ opts = {'input_X': input_x, 'reduction_filter': 64, 'tower_filter': 32, 'tower_kernel': (3, 3)}
+ tower = base.create_conv_tower(**opts)
+ # check second element of tower
+ assert base.part_of_block == 1
+ assert isinstance(tower, tf.Tensor)
+ assert tower.name == 'Block_0a_3x3/Relu:0'
+ assert isinstance(tower._keras_history[0], keras.layers.Conv2D)
+ assert tower._keras_history[0].filters == 32
+ assert isinstance(tower._keras_history[0].input, tf.Tensor)
+ assert tower._keras_history[0].padding == 'same'
+ assert tower._keras_history[0].kernel_size == (3, 3)
+ assert tower._keras_history[0].strides == (1, 1)
+ # check first element of tower
+ assert isinstance(tower._keras_history[0].input._keras_history[0], keras.layers.Conv2D)
+ assert tower._keras_history[0].input._keras_history[0].filters == 64
+ assert tower._keras_history[0].input._keras_history[0].kernel_size == (1, 1)
+ assert tower._keras_history[0].input._keras_history[0].padding == 'same'
+ assert tower._keras_history[0].input._keras_history[0].name == 'Block_0a_1x1'
+ assert tower._keras_history[0].input._keras_history[0].input._keras_shape == (None, 32, 32, 3)
+
+ def test_create_conv_tower_1x1(self, base, input_x):
+ opts = {'input_X': input_x, 'reduction_filter': 64, 'tower_filter': 32, 'tower_kernel': (1, 1)}
+ tower = base.create_conv_tower(**opts)
+ # check second element of tower
+ assert base.part_of_block == 1
+ assert isinstance(tower, tf.Tensor)
+ assert tower.name == 'Block_0a_1x1_1/Relu:0'
+ assert isinstance(tower._keras_history[0], keras.layers.Conv2D)
+ assert tower._keras_history[0].filters == 32
+ assert isinstance(tower._keras_history[0].input, tf.Tensor)
+ assert tower._keras_history[0].input._keras_shape == (None, 32, 32, 3)
+ assert tower._keras_history[0].padding == 'same'
+ assert tower._keras_history[0].kernel_size == (1, 1)
+ assert tower._keras_history[0].strides == (1, 1)
+
+ def test_create_conv_towers(self, base, input_x):
+ opts = {'input_X': input_x, 'reduction_filter': 64, 'tower_filter': 32, 'tower_kernel': (3, 3)}
+ _ = base.create_conv_tower(**opts)
+ tower = base.create_conv_tower(**opts)
+ assert base.part_of_block == 2
+ assert tower.name == 'Block_0b_3x3/Relu:0'
+
+ def test_create_pool_tower(self, base, input_x):
+ opts = {'input_X': input_x, 'pool_kernel': (3, 3), 'tower_filter': 32}
+ tower = base.create_pool_tower(**opts)
+ # check second element of tower
+ assert base.part_of_block == 0
+ assert isinstance(tower, tf.Tensor)
+ assert tower.name == 'conv2d_1/Relu:0'
+ assert isinstance(tower._keras_history[0], keras.layers.Conv2D)
+ assert tower._keras_history[0].filters == 32
+ assert tower._keras_history[0].padding == 'same'
+ assert tower._keras_history[0].kernel_size == (1, 1)
+ assert tower._keras_history[0].strides == (1, 1)
+ # check first element of tower
+ assert isinstance(tower._keras_history[0].input, tf.Tensor)
+ assert tower._keras_history[0].input._keras_history[0].pool_size == (3, 3)
+ assert tower._keras_history[0].input._keras_history[0].padding == 'same'
+ assert tower._keras_history[0].input._keras_history[0].name == 'max_pooling2d_1'
+
+ def test_inception_block(self, base, input_x):
+ conv = {'tower_1': {'reduction_filter': 64, 'tower_kernel': (3, 3), 'tower_filter': 64},
+ 'tower_2': {'reduction_filter': 64, 'tower_kernel': (5, 5), 'tower_filter': 64}}
+ pool = {'pool_kernel': (3, 3), 'tower_filter': 64}
+ opts = {'input_X': input_x, 'tower_conv_parts': conv, 'tower_pool_parts': pool}
+ block = base.inception_block(**opts)
+ assert base.number_of_blocks == 1
+ concatenated = block._keras_history[0].input
+ assert len(concatenated) == 3
+ block_1a, block_1b, block_pool = concatenated
+ assert block_1a.name == 'Block_1a_3x3/Relu:0'
+ assert block_1b.name == 'Block_1b_5x5/Relu:0'
+ assert block_pool.name == 'conv2d_2/Relu:0'
+ # next block
+ opts['input_X'] = block
+ block = base.inception_block(**opts)
+ assert base.number_of_blocks == 2
+ concatenated = block._keras_history[0].input
+ assert len(concatenated) == 3
+ block_1a, block_1b, block_pool = concatenated
+ assert block_1a.name == 'Block_2a_3x3/Relu:0'
+ assert block_1b.name == 'Block_2b_5x5/Relu:0'
+ assert block_pool.name == 'conv2d_3/Relu:0'