Model: "functional_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 224, 224, 3)] 0
_________________________________________________________________
conv1_pad (ZeroPadding2D) (None, 226, 226, 3) 0
_________________________________________________________________
conv1 (Conv2D) (None, 112, 112, 24) 648
_________________________________________________________________
conv1_bn (BatchNormalization (None, 112, 112, 24) 96
_________________________________________________________________
conv1_relu (ReLU) (None, 112, 112, 24) 0
_________________________________________________________________
conv_dw_1 (DepthwiseConv2D) (None, 112, 112, 24) 216
_________________________________________________________________
conv_dw_1_bn (BatchNormaliza (None, 112, 112, 24) 96
_________________________________________________________________
conv_dw_1_relu (ReLU) (None, 112, 112, 24) 0
_________________________________________________________________
conv_pw_1 (Conv2D) (None, 112, 112, 48) 1152
_________________________________________________________________
conv_pw_1_bn (BatchNormaliza (None, 112, 112, 48) 192
_________________________________________________________________
conv_pw_1_relu (ReLU) (None, 112, 112, 48) 0
_________________________________________________________________
conv_pad_2 (ZeroPadding2D) (None, 114, 114, 48) 0
_________________________________________________________________
conv_dw_2 (DepthwiseConv2D) (None, 56, 56, 48) 432
_________________________________________________________________
conv_dw_2_bn (BatchNormaliza (None, 56, 56, 48) 192
_________________________________________________________________
conv_dw_2_relu (ReLU) (None, 56, 56, 48) 0
_________________________________________________________________
conv_pw_2 (Conv2D) (None, 56, 56, 96) 4608
_________________________________________________________________
conv_pw_2_bn (BatchNormaliza (None, 56, 56, 96) 384
_________________________________________________________________
conv_pw_2_relu (ReLU) (None, 56, 56, 96) 0
_________________________________________________________________
conv_dw_3 (DepthwiseConv2D) (None, 56, 56, 96) 864
_________________________________________________________________
conv_dw_3_bn (BatchNormaliza (None, 56, 56, 96) 384
_________________________________________________________________
conv_dw_3_relu (ReLU) (None, 56, 56, 96) 0
_________________________________________________________________
conv_pw_3 (Conv2D) (None, 56, 56, 96) 9216
_________________________________________________________________
conv_pw_3_bn (BatchNormaliza (None, 56, 56, 96) 384
_________________________________________________________________
conv_pw_3_relu (ReLU) (None, 56, 56, 96) 0
_________________________________________________________________
conv_pad_4 (ZeroPadding2D) (None, 58, 58, 96) 0
_________________________________________________________________
conv_dw_4 (DepthwiseConv2D) (None, 28, 28, 96) 864
_________________________________________________________________
conv_dw_4_bn (BatchNormaliza (None, 28, 28, 96) 384
_________________________________________________________________
conv_dw_4_relu (ReLU) (None, 28, 28, 96) 0
_________________________________________________________________
conv_pw_4 (Conv2D) (None, 28, 28, 192) 18432
_________________________________________________________________
conv_pw_4_bn (BatchNormaliza (None, 28, 28, 192) 768
_________________________________________________________________
conv_pw_4_relu (ReLU) (None, 28, 28, 192) 0
_________________________________________________________________
conv_dw_5 (DepthwiseConv2D) (None, 28, 28, 192) 1728
_________________________________________________________________
conv_dw_5_bn (BatchNormaliza (None, 28, 28, 192) 768
_________________________________________________________________
conv_dw_5_relu (ReLU) (None, 28, 28, 192) 0
_________________________________________________________________
conv_pw_5 (Conv2D) (None, 28, 28, 192) 36864
_________________________________________________________________
conv_pw_5_bn (BatchNormaliza (None, 28, 28, 192) 768
_________________________________________________________________
conv_pw_5_relu (ReLU) (None, 28, 28, 192) 0
_________________________________________________________________
conv_pad_6 (ZeroPadding2D) (None, 30, 30, 192) 0
_________________________________________________________________
conv_dw_6 (DepthwiseConv2D) (None, 14, 14, 192) 1728
_________________________________________________________________
conv_dw_6_bn (BatchNormaliza (None, 14, 14, 192) 768
_________________________________________________________________
conv_dw_6_relu (ReLU) (None, 14, 14, 192) 0
_________________________________________________________________
conv_pw_6 (Conv2D) (None, 14, 14, 384) 73728
_________________________________________________________________
conv_pw_6_bn (BatchNormaliza (None, 14, 14, 384) 1536
_________________________________________________________________
conv_pw_6_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_dw_7 (DepthwiseConv2D) (None, 14, 14, 384) 3456
_________________________________________________________________
conv_dw_7_bn (BatchNormaliza (None, 14, 14, 384) 1536
_________________________________________________________________
conv_dw_7_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_pw_7 (Conv2D) (None, 14, 14, 384) 147456
_________________________________________________________________
conv_pw_7_bn (BatchNormaliza (None, 14, 14, 384) 1536
_________________________________________________________________
conv_pw_7_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_dw_8 (DepthwiseConv2D) (None, 14, 14, 384) 3456
_________________________________________________________________
conv_dw_8_bn (BatchNormaliza (None, 14, 14, 384) 1536
_________________________________________________________________
conv_dw_8_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_pw_8 (Conv2D) (None, 14, 14, 384) 147456
_________________________________________________________________
conv_pw_8_bn (BatchNormaliza (None, 14, 14, 384) 1536
_________________________________________________________________
conv_pw_8_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_dw_9 (DepthwiseConv2D) (None, 14, 14, 384) 3456
_________________________________________________________________
conv_dw_9_bn (BatchNormaliza (None, 14, 14, 384) 1536
_________________________________________________________________
conv_dw_9_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_pw_9 (Conv2D) (None, 14, 14, 384) 147456
_________________________________________________________________
conv_pw_9_bn (BatchNormaliza (None, 14, 14, 384) 1536
_________________________________________________________________
conv_pw_9_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 384) 3456
_________________________________________________________________
conv_dw_10_bn (BatchNormaliz (None, 14, 14, 384) 1536
_________________________________________________________________
conv_dw_10_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_pw_10 (Conv2D) (None, 14, 14, 384) 147456
_________________________________________________________________
conv_pw_10_bn (BatchNormaliz (None, 14, 14, 384) 1536
_________________________________________________________________
conv_pw_10_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 384) 3456
_________________________________________________________________
conv_dw_11_bn (BatchNormaliz (None, 14, 14, 384) 1536
_________________________________________________________________
conv_dw_11_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_pw_11 (Conv2D) (None, 14, 14, 384) 147456
_________________________________________________________________
conv_pw_11_bn (BatchNormaliz (None, 14, 14, 384) 1536
_________________________________________________________________
conv_pw_11_relu (ReLU) (None, 14, 14, 384) 0
_________________________________________________________________
conv_pad_12 (ZeroPadding2D) (None, 16, 16, 384) 0
_________________________________________________________________
conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 384) 3456
_________________________________________________________________
conv_dw_12_bn (BatchNormaliz (None, 7, 7, 384) 1536
_________________________________________________________________
conv_dw_12_relu (ReLU) (None, 7, 7, 384) 0
_________________________________________________________________
conv_pw_12 (Conv2D) (None, 7, 7, 768) 294912
_________________________________________________________________
conv_pw_12_bn (BatchNormaliz (None, 7, 7, 768) 3072
_________________________________________________________________
conv_pw_12_relu (ReLU) (None, 7, 7, 768) 0
_________________________________________________________________
conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 768) 6912
_________________________________________________________________
conv_dw_13_bn (BatchNormaliz (None, 7, 7, 768) 3072
_________________________________________________________________
conv_dw_13_relu (ReLU) (None, 7, 7, 768) 0
_________________________________________________________________
conv_pw_13 (Conv2D) (None, 7, 7, 768) 589824
_________________________________________________________________
conv_pw_13_bn (BatchNormaliz (None, 7, 7, 768) 3072
_________________________________________________________________
conv_pw_13_relu (ReLU) (None, 7, 7, 768) 0
_________________________________________________________________
global_average_pooling2d (Gl (None, 768) 0
_________________________________________________________________
dense (Dense) (None, 100) 76900
_________________________________________________________________
dropout (Dropout) (None, 100) 0
_________________________________________________________________
dense_1 (Dense) (None, 50) 5050
_________________________________________________________________
dropout_1 (Dropout) (None, 50) 0
_________________________________________________________________
dense_2 (Dense) (None, 4) 204
=================================================================
Total params: 1,915,130
Trainable params: 82,154
Non-trainable params: 1,832,976
_________________________________________________________________
WARNING:tensorflow:From <ipython-input-12-ad3e89ccc49c>:4: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
Please use Model.fit, which supports generators.
Epoch 1/20
36/36 [==============================] - 1s 39ms/step - loss: 1.1525 - accuracy: 0.5313
Epoch 2/20
36/36 [==============================] - 1s 31ms/step - loss: 0.4755 - accuracy: 0.8191
Epoch 3/20
36/36 [==============================] - 1s 30ms/step - loss: 0.2688 - accuracy: 0.9052
Epoch 4/20
36/36 [==============================] - 1s 31ms/step - loss: 0.1548 - accuracy: 0.9496
Epoch 5/20
36/36 [==============================] - 1s 30ms/step - loss: 0.1056 - accuracy: 0.9661
Epoch 6/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0807 - accuracy: 0.9800
Epoch 7/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0719 - accuracy: 0.9757
Epoch 8/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0606 - accuracy: 0.9870
Epoch 9/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0454 - accuracy: 0.9843
Epoch 10/20
36/36 [==============================] - 1s 30ms/step - loss: 0.0407 - accuracy: 0.9878
Epoch 11/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0358 - accuracy: 0.9878
Epoch 12/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0299 - accuracy: 0.9913
Epoch 13/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0306 - accuracy: 0.9930
Epoch 14/20
36/36 [==============================] - 1s 30ms/step - loss: 0.0265 - accuracy: 0.9939
Epoch 15/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0212 - accuracy: 0.9939
Epoch 16/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0191 - accuracy: 0.9948
Epoch 17/20
36/36 [==============================] - 1s 30ms/step - loss: 0.0202 - accuracy: 0.9939
Epoch 18/20
36/36 [==============================] - 1s 30ms/step - loss: 0.0259 - accuracy: 0.9904
Epoch 19/20
36/36 [==============================] - 1s 30ms/step - loss: 0.0149 - accuracy: 0.9957
Epoch 20/20
36/36 [==============================] - 1s 31ms/step - loss: 0.0173 - accuracy: 0.9957