mirror of
https://github.com/MarcosRodrigoT/ViT-Face-Recognition.git
synced 2025-12-30 08:02:29 +00:00
390 lines
59 KiB
Plaintext
390 lines
59 KiB
Plaintext
[INFO] Num images for train: 2827701 -> train_ds: 2827701
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[INFO] Num images for validation: 157094 -> val_ds: 157094
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[INFO] Num images for test: 157094 -> test_ds: 157095
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Model: "MobileNetV2"
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______________________________________________________________________________________________________________________________________________________
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Layer (type) Output Shape Param # Connected to
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======================================================================================================================================================
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input_1 (InputLayer) [(None, 224, 224, 3)] 0 []
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Conv1 (Conv2D) (None, 112, 112, 32) 864 ['input_1[0][0]']
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bn_Conv1 (BatchNormalization) (None, 112, 112, 32) 128 ['Conv1[0][0]']
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Conv1_relu (ReLU) (None, 112, 112, 32) 0 ['bn_Conv1[0][0]']
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expanded_conv_depthwise (DepthwiseConv2D) (None, 112, 112, 32) 288 ['Conv1_relu[0][0]']
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expanded_conv_depthwise_BN (BatchNormalization) (None, 112, 112, 32) 128 ['expanded_conv_depthwise[0][0]']
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expanded_conv_depthwise_relu (ReLU) (None, 112, 112, 32) 0 ['expanded_conv_depthwise_BN[0][0]']
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expanded_conv_project (Conv2D) (None, 112, 112, 16) 512 ['expanded_conv_depthwise_relu[0][0]']
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expanded_conv_project_BN (BatchNormalization) (None, 112, 112, 16) 64 ['expanded_conv_project[0][0]']
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block_1_expand (Conv2D) (None, 112, 112, 96) 1536 ['expanded_conv_project_BN[0][0]']
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block_1_expand_BN (BatchNormalization) (None, 112, 112, 96) 384 ['block_1_expand[0][0]']
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block_1_expand_relu (ReLU) (None, 112, 112, 96) 0 ['block_1_expand_BN[0][0]']
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block_1_pad (ZeroPadding2D) (None, 113, 113, 96) 0 ['block_1_expand_relu[0][0]']
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block_1_depthwise (DepthwiseConv2D) (None, 56, 56, 96) 864 ['block_1_pad[0][0]']
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block_1_depthwise_BN (BatchNormalization) (None, 56, 56, 96) 384 ['block_1_depthwise[0][0]']
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block_1_depthwise_relu (ReLU) (None, 56, 56, 96) 0 ['block_1_depthwise_BN[0][0]']
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block_1_project (Conv2D) (None, 56, 56, 24) 2304 ['block_1_depthwise_relu[0][0]']
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block_1_project_BN (BatchNormalization) (None, 56, 56, 24) 96 ['block_1_project[0][0]']
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block_2_expand (Conv2D) (None, 56, 56, 144) 3456 ['block_1_project_BN[0][0]']
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block_2_expand_BN (BatchNormalization) (None, 56, 56, 144) 576 ['block_2_expand[0][0]']
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block_2_expand_relu (ReLU) (None, 56, 56, 144) 0 ['block_2_expand_BN[0][0]']
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block_2_depthwise (DepthwiseConv2D) (None, 56, 56, 144) 1296 ['block_2_expand_relu[0][0]']
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block_2_depthwise_BN (BatchNormalization) (None, 56, 56, 144) 576 ['block_2_depthwise[0][0]']
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block_2_depthwise_relu (ReLU) (None, 56, 56, 144) 0 ['block_2_depthwise_BN[0][0]']
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block_2_project (Conv2D) (None, 56, 56, 24) 3456 ['block_2_depthwise_relu[0][0]']
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block_2_project_BN (BatchNormalization) (None, 56, 56, 24) 96 ['block_2_project[0][0]']
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block_2_add (Add) (None, 56, 56, 24) 0 ['block_1_project_BN[0][0]',
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'block_2_project_BN[0][0]']
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block_3_expand (Conv2D) (None, 56, 56, 144) 3456 ['block_2_add[0][0]']
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block_3_expand_BN (BatchNormalization) (None, 56, 56, 144) 576 ['block_3_expand[0][0]']
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block_3_expand_relu (ReLU) (None, 56, 56, 144) 0 ['block_3_expand_BN[0][0]']
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block_3_pad (ZeroPadding2D) (None, 57, 57, 144) 0 ['block_3_expand_relu[0][0]']
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block_3_depthwise (DepthwiseConv2D) (None, 28, 28, 144) 1296 ['block_3_pad[0][0]']
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block_3_depthwise_BN (BatchNormalization) (None, 28, 28, 144) 576 ['block_3_depthwise[0][0]']
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block_3_depthwise_relu (ReLU) (None, 28, 28, 144) 0 ['block_3_depthwise_BN[0][0]']
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block_3_project (Conv2D) (None, 28, 28, 32) 4608 ['block_3_depthwise_relu[0][0]']
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block_3_project_BN (BatchNormalization) (None, 28, 28, 32) 128 ['block_3_project[0][0]']
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block_4_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_3_project_BN[0][0]']
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block_4_expand_BN (BatchNormalization) (None, 28, 28, 192) 768 ['block_4_expand[0][0]']
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block_4_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_4_expand_BN[0][0]']
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block_4_depthwise (DepthwiseConv2D) (None, 28, 28, 192) 1728 ['block_4_expand_relu[0][0]']
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block_4_depthwise_BN (BatchNormalization) (None, 28, 28, 192) 768 ['block_4_depthwise[0][0]']
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block_4_depthwise_relu (ReLU) (None, 28, 28, 192) 0 ['block_4_depthwise_BN[0][0]']
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block_4_project (Conv2D) (None, 28, 28, 32) 6144 ['block_4_depthwise_relu[0][0]']
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block_4_project_BN (BatchNormalization) (None, 28, 28, 32) 128 ['block_4_project[0][0]']
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block_4_add (Add) (None, 28, 28, 32) 0 ['block_3_project_BN[0][0]',
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'block_4_project_BN[0][0]']
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block_5_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_4_add[0][0]']
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block_5_expand_BN (BatchNormalization) (None, 28, 28, 192) 768 ['block_5_expand[0][0]']
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block_5_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_5_expand_BN[0][0]']
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block_5_depthwise (DepthwiseConv2D) (None, 28, 28, 192) 1728 ['block_5_expand_relu[0][0]']
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block_5_depthwise_BN (BatchNormalization) (None, 28, 28, 192) 768 ['block_5_depthwise[0][0]']
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block_5_depthwise_relu (ReLU) (None, 28, 28, 192) 0 ['block_5_depthwise_BN[0][0]']
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block_5_project (Conv2D) (None, 28, 28, 32) 6144 ['block_5_depthwise_relu[0][0]']
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block_5_project_BN (BatchNormalization) (None, 28, 28, 32) 128 ['block_5_project[0][0]']
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block_5_add (Add) (None, 28, 28, 32) 0 ['block_4_add[0][0]',
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'block_5_project_BN[0][0]']
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block_6_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_5_add[0][0]']
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block_6_expand_BN (BatchNormalization) (None, 28, 28, 192) 768 ['block_6_expand[0][0]']
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block_6_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_6_expand_BN[0][0]']
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block_6_pad (ZeroPadding2D) (None, 29, 29, 192) 0 ['block_6_expand_relu[0][0]']
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block_6_depthwise (DepthwiseConv2D) (None, 14, 14, 192) 1728 ['block_6_pad[0][0]']
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block_6_depthwise_BN (BatchNormalization) (None, 14, 14, 192) 768 ['block_6_depthwise[0][0]']
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block_6_depthwise_relu (ReLU) (None, 14, 14, 192) 0 ['block_6_depthwise_BN[0][0]']
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block_6_project (Conv2D) (None, 14, 14, 64) 12288 ['block_6_depthwise_relu[0][0]']
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block_6_project_BN (BatchNormalization) (None, 14, 14, 64) 256 ['block_6_project[0][0]']
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block_7_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_6_project_BN[0][0]']
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block_7_expand_BN (BatchNormalization) (None, 14, 14, 384) 1536 ['block_7_expand[0][0]']
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block_7_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_7_expand_BN[0][0]']
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block_7_depthwise (DepthwiseConv2D) (None, 14, 14, 384) 3456 ['block_7_expand_relu[0][0]']
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block_7_depthwise_BN (BatchNormalization) (None, 14, 14, 384) 1536 ['block_7_depthwise[0][0]']
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block_7_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_7_depthwise_BN[0][0]']
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block_7_project (Conv2D) (None, 14, 14, 64) 24576 ['block_7_depthwise_relu[0][0]']
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block_7_project_BN (BatchNormalization) (None, 14, 14, 64) 256 ['block_7_project[0][0]']
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block_7_add (Add) (None, 14, 14, 64) 0 ['block_6_project_BN[0][0]',
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'block_7_project_BN[0][0]']
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block_8_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_7_add[0][0]']
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block_8_expand_BN (BatchNormalization) (None, 14, 14, 384) 1536 ['block_8_expand[0][0]']
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block_8_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_8_expand_BN[0][0]']
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block_8_depthwise (DepthwiseConv2D) (None, 14, 14, 384) 3456 ['block_8_expand_relu[0][0]']
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block_8_depthwise_BN (BatchNormalization) (None, 14, 14, 384) 1536 ['block_8_depthwise[0][0]']
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block_8_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_8_depthwise_BN[0][0]']
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block_8_project (Conv2D) (None, 14, 14, 64) 24576 ['block_8_depthwise_relu[0][0]']
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block_8_project_BN (BatchNormalization) (None, 14, 14, 64) 256 ['block_8_project[0][0]']
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block_8_add (Add) (None, 14, 14, 64) 0 ['block_7_add[0][0]',
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'block_8_project_BN[0][0]']
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block_9_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_8_add[0][0]']
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block_9_expand_BN (BatchNormalization) (None, 14, 14, 384) 1536 ['block_9_expand[0][0]']
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block_9_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_9_expand_BN[0][0]']
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block_9_depthwise (DepthwiseConv2D) (None, 14, 14, 384) 3456 ['block_9_expand_relu[0][0]']
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block_9_depthwise_BN (BatchNormalization) (None, 14, 14, 384) 1536 ['block_9_depthwise[0][0]']
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block_9_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_9_depthwise_BN[0][0]']
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block_9_project (Conv2D) (None, 14, 14, 64) 24576 ['block_9_depthwise_relu[0][0]']
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block_9_project_BN (BatchNormalization) (None, 14, 14, 64) 256 ['block_9_project[0][0]']
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block_9_add (Add) (None, 14, 14, 64) 0 ['block_8_add[0][0]',
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'block_9_project_BN[0][0]']
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block_10_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_9_add[0][0]']
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block_10_expand_BN (BatchNormalization) (None, 14, 14, 384) 1536 ['block_10_expand[0][0]']
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block_10_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_10_expand_BN[0][0]']
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block_10_depthwise (DepthwiseConv2D) (None, 14, 14, 384) 3456 ['block_10_expand_relu[0][0]']
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block_10_depthwise_BN (BatchNormalization) (None, 14, 14, 384) 1536 ['block_10_depthwise[0][0]']
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block_10_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_10_depthwise_BN[0][0]']
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block_10_project (Conv2D) (None, 14, 14, 96) 36864 ['block_10_depthwise_relu[0][0]']
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block_10_project_BN (BatchNormalization) (None, 14, 14, 96) 384 ['block_10_project[0][0]']
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block_11_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_10_project_BN[0][0]']
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block_11_expand_BN (BatchNormalization) (None, 14, 14, 576) 2304 ['block_11_expand[0][0]']
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block_11_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_11_expand_BN[0][0]']
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block_11_depthwise (DepthwiseConv2D) (None, 14, 14, 576) 5184 ['block_11_expand_relu[0][0]']
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block_11_depthwise_BN (BatchNormalization) (None, 14, 14, 576) 2304 ['block_11_depthwise[0][0]']
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block_11_depthwise_relu (ReLU) (None, 14, 14, 576) 0 ['block_11_depthwise_BN[0][0]']
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block_11_project (Conv2D) (None, 14, 14, 96) 55296 ['block_11_depthwise_relu[0][0]']
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block_11_project_BN (BatchNormalization) (None, 14, 14, 96) 384 ['block_11_project[0][0]']
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block_11_add (Add) (None, 14, 14, 96) 0 ['block_10_project_BN[0][0]',
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'block_11_project_BN[0][0]']
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block_12_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_11_add[0][0]']
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block_12_expand_BN (BatchNormalization) (None, 14, 14, 576) 2304 ['block_12_expand[0][0]']
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block_12_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_12_expand_BN[0][0]']
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block_12_depthwise (DepthwiseConv2D) (None, 14, 14, 576) 5184 ['block_12_expand_relu[0][0]']
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block_12_depthwise_BN (BatchNormalization) (None, 14, 14, 576) 2304 ['block_12_depthwise[0][0]']
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block_12_depthwise_relu (ReLU) (None, 14, 14, 576) 0 ['block_12_depthwise_BN[0][0]']
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block_12_project (Conv2D) (None, 14, 14, 96) 55296 ['block_12_depthwise_relu[0][0]']
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block_12_project_BN (BatchNormalization) (None, 14, 14, 96) 384 ['block_12_project[0][0]']
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block_12_add (Add) (None, 14, 14, 96) 0 ['block_11_add[0][0]',
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'block_12_project_BN[0][0]']
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block_13_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_12_add[0][0]']
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block_13_expand_BN (BatchNormalization) (None, 14, 14, 576) 2304 ['block_13_expand[0][0]']
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block_13_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_13_expand_BN[0][0]']
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block_13_pad (ZeroPadding2D) (None, 15, 15, 576) 0 ['block_13_expand_relu[0][0]']
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block_13_depthwise (DepthwiseConv2D) (None, 7, 7, 576) 5184 ['block_13_pad[0][0]']
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block_13_depthwise_BN (BatchNormalization) (None, 7, 7, 576) 2304 ['block_13_depthwise[0][0]']
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block_13_depthwise_relu (ReLU) (None, 7, 7, 576) 0 ['block_13_depthwise_BN[0][0]']
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block_13_project (Conv2D) (None, 7, 7, 160) 92160 ['block_13_depthwise_relu[0][0]']
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block_13_project_BN (BatchNormalization) (None, 7, 7, 160) 640 ['block_13_project[0][0]']
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block_14_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_13_project_BN[0][0]']
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block_14_expand_BN (BatchNormalization) (None, 7, 7, 960) 3840 ['block_14_expand[0][0]']
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block_14_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_14_expand_BN[0][0]']
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block_14_depthwise (DepthwiseConv2D) (None, 7, 7, 960) 8640 ['block_14_expand_relu[0][0]']
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block_14_depthwise_BN (BatchNormalization) (None, 7, 7, 960) 3840 ['block_14_depthwise[0][0]']
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block_14_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_14_depthwise_BN[0][0]']
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block_14_project (Conv2D) (None, 7, 7, 160) 153600 ['block_14_depthwise_relu[0][0]']
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block_14_project_BN (BatchNormalization) (None, 7, 7, 160) 640 ['block_14_project[0][0]']
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block_14_add (Add) (None, 7, 7, 160) 0 ['block_13_project_BN[0][0]',
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'block_14_project_BN[0][0]']
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block_15_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_14_add[0][0]']
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block_15_expand_BN (BatchNormalization) (None, 7, 7, 960) 3840 ['block_15_expand[0][0]']
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block_15_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_15_expand_BN[0][0]']
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block_15_depthwise (DepthwiseConv2D) (None, 7, 7, 960) 8640 ['block_15_expand_relu[0][0]']
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block_15_depthwise_BN (BatchNormalization) (None, 7, 7, 960) 3840 ['block_15_depthwise[0][0]']
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block_15_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_15_depthwise_BN[0][0]']
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block_15_project (Conv2D) (None, 7, 7, 160) 153600 ['block_15_depthwise_relu[0][0]']
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block_15_project_BN (BatchNormalization) (None, 7, 7, 160) 640 ['block_15_project[0][0]']
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block_15_add (Add) (None, 7, 7, 160) 0 ['block_14_add[0][0]',
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'block_15_project_BN[0][0]']
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block_16_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_15_add[0][0]']
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block_16_expand_BN (BatchNormalization) (None, 7, 7, 960) 3840 ['block_16_expand[0][0]']
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block_16_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_16_expand_BN[0][0]']
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block_16_depthwise (DepthwiseConv2D) (None, 7, 7, 960) 8640 ['block_16_expand_relu[0][0]']
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block_16_depthwise_BN (BatchNormalization) (None, 7, 7, 960) 3840 ['block_16_depthwise[0][0]']
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block_16_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_16_depthwise_BN[0][0]']
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block_16_project (Conv2D) (None, 7, 7, 320) 307200 ['block_16_depthwise_relu[0][0]']
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block_16_project_BN (BatchNormalization) (None, 7, 7, 320) 1280 ['block_16_project[0][0]']
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Conv_1 (Conv2D) (None, 7, 7, 1280) 409600 ['block_16_project_BN[0][0]']
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Conv_1_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['Conv_1[0][0]']
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out_relu (ReLU) (None, 7, 7, 1280) 0 ['Conv_1_bn[0][0]']
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global_average_pooling2d (GlobalAveragePooling2 (None, 1280) 0 ['out_relu[0][0]']
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D)
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dense (Dense) (None, 8631) 11056311 ['global_average_pooling2d[0][0]']
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======================================================================================================================================================
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Total params: 13,314,295
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Trainable params: 13,280,183
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Non-trainable params: 34,112
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______________________________________________________________________________________________________________________________________________________
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Epoch 1/25
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Epoch 00001: val_accuracy improved from -inf to 0.70027, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3446s 311ms/step - loss: 2.9497 - accuracy: 0.4908 - top-5-accuracy: 0.6538 - top-10-accuracy: 0.7119 - top-100-accuracy: 0.8687 - val_loss: 1.4240 - val_accuracy: 0.7003 - val_top-5-accuracy: 0.8539 - val_top-10-accuracy: 0.8953 - val_top-100-accuracy: 0.9759
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Epoch 2/25
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Epoch 00002: val_accuracy improved from 0.70027 to 0.83601, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3451s 312ms/step - loss: 0.9672 - accuracy: 0.7959 - top-5-accuracy: 0.9071 - top-10-accuracy: 0.9348 - top-100-accuracy: 0.9855 - val_loss: 0.7466 - val_accuracy: 0.8360 - val_top-5-accuracy: 0.9333 - val_top-10-accuracy: 0.9547 - val_top-100-accuracy: 0.9909
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Epoch 3/25
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Epoch 00003: val_accuracy improved from 0.83601 to 0.87064, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3461s 313ms/step - loss: 0.6250 - accuracy: 0.8633 - top-5-accuracy: 0.9446 - top-10-accuracy: 0.9626 - top-100-accuracy: 0.9925 - val_loss: 0.5745 - val_accuracy: 0.8706 - val_top-5-accuracy: 0.9524 - val_top-10-accuracy: 0.9691 - val_top-100-accuracy: 0.9943
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Epoch 4/25
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Epoch 00004: val_accuracy improved from 0.87064 to 0.90236, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3451s 312ms/step - loss: 0.4612 - accuracy: 0.8969 - top-5-accuracy: 0.9619 - top-10-accuracy: 0.9749 - top-100-accuracy: 0.9952 - val_loss: 0.4232 - val_accuracy: 0.9024 - val_top-5-accuracy: 0.9674 - val_top-10-accuracy: 0.9791 - val_top-100-accuracy: 0.9964
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Epoch 5/25
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Epoch 00005: val_accuracy improved from 0.90236 to 0.92468, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3468s 314ms/step - loss: 0.3604 - accuracy: 0.9181 - top-5-accuracy: 0.9721 - top-10-accuracy: 0.9819 - top-100-accuracy: 0.9968 - val_loss: 0.3239 - val_accuracy: 0.9247 - val_top-5-accuracy: 0.9771 - val_top-10-accuracy: 0.9855 - val_top-100-accuracy: 0.9976
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Epoch 6/25
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Epoch 00006: val_accuracy improved from 0.92468 to 0.93362, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3455s 313ms/step - loss: 0.2913 - accuracy: 0.9328 - top-5-accuracy: 0.9787 - top-10-accuracy: 0.9866 - top-100-accuracy: 0.9978 - val_loss: 0.2783 - val_accuracy: 0.9336 - val_top-5-accuracy: 0.9816 - val_top-10-accuracy: 0.9886 - val_top-100-accuracy: 0.9984
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Epoch 7/25
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Epoch 00007: val_accuracy did not improve from 0.93362
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11046/11046 [==============================] - 3436s 311ms/step - loss: 0.2407 - accuracy: 0.9436 - top-5-accuracy: 0.9835 - top-10-accuracy: 0.9898 - top-100-accuracy: 0.9984 - val_loss: 0.2714 - val_accuracy: 0.9319 - val_top-5-accuracy: 0.9826 - val_top-10-accuracy: 0.9900 - val_top-100-accuracy: 0.9986
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Epoch 8/25
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Epoch 00008: val_accuracy improved from 0.93362 to 0.95498, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3440s 311ms/step - loss: 0.2013 - accuracy: 0.9522 - top-5-accuracy: 0.9872 - top-10-accuracy: 0.9922 - top-100-accuracy: 0.9989 - val_loss: 0.1851 - val_accuracy: 0.9550 - val_top-5-accuracy: 0.9900 - val_top-10-accuracy: 0.9942 - val_top-100-accuracy: 0.9993
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Epoch 9/25
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Epoch 00009: val_accuracy did not improve from 0.95498
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11046/11046 [==============================] - 3459s 313ms/step - loss: 0.1699 - accuracy: 0.9590 - top-5-accuracy: 0.9899 - top-10-accuracy: 0.9940 - top-100-accuracy: 0.9992 - val_loss: 0.1860 - val_accuracy: 0.9511 - val_top-5-accuracy: 0.9903 - val_top-10-accuracy: 0.9950 - val_top-100-accuracy: 0.9994
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Epoch 10/25
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Epoch 00010: val_accuracy improved from 0.95498 to 0.95772, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3447s 312ms/step - loss: 0.1451 - accuracy: 0.9644 - top-5-accuracy: 0.9920 - top-10-accuracy: 0.9954 - top-100-accuracy: 0.9994 - val_loss: 0.1597 - val_accuracy: 0.9577 - val_top-5-accuracy: 0.9926 - val_top-10-accuracy: 0.9961 - val_top-100-accuracy: 0.9995
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Epoch 11/25
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Epoch 00011: val_accuracy did not improve from 0.95772
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11046/11046 [==============================] - 3459s 313ms/step - loss: 0.1248 - accuracy: 0.9689 - top-5-accuracy: 0.9936 - top-10-accuracy: 0.9964 - top-100-accuracy: 0.9996 - val_loss: 0.1626 - val_accuracy: 0.9551 - val_top-5-accuracy: 0.9927 - val_top-10-accuracy: 0.9966 - val_top-100-accuracy: 0.9997
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Epoch 12/25
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Epoch 00012: val_accuracy improved from 0.95772 to 0.97526, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3472s 314ms/step - loss: 0.1077 - accuracy: 0.9728 - top-5-accuracy: 0.9950 - top-10-accuracy: 0.9973 - top-100-accuracy: 0.9997 - val_loss: 0.0944 - val_accuracy: 0.9753 - val_top-5-accuracy: 0.9967 - val_top-10-accuracy: 0.9983 - val_top-100-accuracy: 0.9999
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Epoch 13/25
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Epoch 00013: val_accuracy did not improve from 0.97526
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11046/11046 [==============================] - 3464s 314ms/step - loss: 0.0937 - accuracy: 0.9759 - top-5-accuracy: 0.9961 - top-10-accuracy: 0.9979 - top-100-accuracy: 0.9998 - val_loss: 0.0970 - val_accuracy: 0.9736 - val_top-5-accuracy: 0.9967 - val_top-10-accuracy: 0.9984 - val_top-100-accuracy: 0.9999
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Epoch 14/25
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Epoch 00014: val_accuracy improved from 0.97526 to 0.97551, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3473s 314ms/step - loss: 0.0820 - accuracy: 0.9786 - top-5-accuracy: 0.9969 - top-10-accuracy: 0.9984 - top-100-accuracy: 0.9999 - val_loss: 0.0881 - val_accuracy: 0.9755 - val_top-5-accuracy: 0.9971 - val_top-10-accuracy: 0.9987 - val_top-100-accuracy: 0.9999
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Epoch 15/25
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Epoch 00015: val_accuracy improved from 0.97551 to 0.97630, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3458s 313ms/step - loss: 0.0726 - accuracy: 0.9809 - top-5-accuracy: 0.9975 - top-10-accuracy: 0.9988 - top-100-accuracy: 0.9999 - val_loss: 0.0846 - val_accuracy: 0.9763 - val_top-5-accuracy: 0.9976 - val_top-10-accuracy: 0.9990 - val_top-100-accuracy: 1.0000
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Epoch 16/25
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Epoch 00016: val_accuracy improved from 0.97630 to 0.97727, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3463s 313ms/step - loss: 0.0643 - accuracy: 0.9829 - top-5-accuracy: 0.9981 - top-10-accuracy: 0.9991 - top-100-accuracy: 0.9999 - val_loss: 0.0778 - val_accuracy: 0.9773 - val_top-5-accuracy: 0.9981 - val_top-10-accuracy: 0.9993 - val_top-100-accuracy: 1.0000
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Epoch 17/25
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Epoch 00017: val_accuracy improved from 0.97727 to 0.97786, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3451s 312ms/step - loss: 0.0572 - accuracy: 0.9846 - top-5-accuracy: 0.9985 - top-10-accuracy: 0.9993 - top-100-accuracy: 1.0000 - val_loss: 0.0758 - val_accuracy: 0.9779 - val_top-5-accuracy: 0.9982 - val_top-10-accuracy: 0.9993 - val_top-100-accuracy: 1.0000
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Epoch 18/25
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Epoch 00018: val_accuracy improved from 0.97786 to 0.97878, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3426s 310ms/step - loss: 0.0517 - accuracy: 0.9860 - top-5-accuracy: 0.9988 - top-10-accuracy: 0.9995 - top-100-accuracy: 1.0000 - val_loss: 0.0705 - val_accuracy: 0.9788 - val_top-5-accuracy: 0.9986 - val_top-10-accuracy: 0.9995 - val_top-100-accuracy: 1.0000
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Epoch 19/25
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Epoch 00019: val_accuracy improved from 0.97878 to 0.97922, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3425s 310ms/step - loss: 0.0468 - accuracy: 0.9872 - top-5-accuracy: 0.9990 - top-10-accuracy: 0.9996 - top-100-accuracy: 1.0000 - val_loss: 0.0694 - val_accuracy: 0.9792 - val_top-5-accuracy: 0.9986 - val_top-10-accuracy: 0.9996 - val_top-100-accuracy: 1.0000
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Epoch 20/25
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Epoch 00020: val_accuracy improved from 0.97922 to 0.98256, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3418s 309ms/step - loss: 0.0424 - accuracy: 0.9882 - top-5-accuracy: 0.9992 - top-10-accuracy: 0.9997 - top-100-accuracy: 1.0000 - val_loss: 0.0584 - val_accuracy: 0.9826 - val_top-5-accuracy: 0.9991 - val_top-10-accuracy: 0.9997 - val_top-100-accuracy: 1.0000
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Epoch 21/25
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Epoch 00021: val_accuracy did not improve from 0.98256
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11046/11046 [==============================] - 3425s 310ms/step - loss: 0.0391 - accuracy: 0.9891 - top-5-accuracy: 0.9993 - top-10-accuracy: 0.9998 - top-100-accuracy: 1.0000 - val_loss: 0.0739 - val_accuracy: 0.9775 - val_top-5-accuracy: 0.9984 - val_top-10-accuracy: 0.9995 - val_top-100-accuracy: 1.0000
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Epoch 22/25
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Epoch 00022: val_accuracy improved from 0.98256 to 0.98781, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3420s 309ms/step - loss: 0.0362 - accuracy: 0.9899 - top-5-accuracy: 0.9995 - top-10-accuracy: 0.9998 - top-100-accuracy: 1.0000 - val_loss: 0.0415 - val_accuracy: 0.9878 - val_top-5-accuracy: 0.9995 - val_top-10-accuracy: 0.9999 - val_top-100-accuracy: 1.0000
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Epoch 23/25
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Epoch 00023: val_accuracy did not improve from 0.98781
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11046/11046 [==============================] - 3420s 310ms/step - loss: 0.0334 - accuracy: 0.9907 - top-5-accuracy: 0.9996 - top-10-accuracy: 0.9999 - top-100-accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9770 - val_top-5-accuracy: 0.9985 - val_top-10-accuracy: 0.9996 - val_top-100-accuracy: 1.0000
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Epoch 24/25
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Epoch 00024: val_accuracy improved from 0.98781 to 0.98899, saving model to ./tmp/checkpoint
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11046/11046 [==============================] - 3436s 311ms/step - loss: 0.0311 - accuracy: 0.9912 - top-5-accuracy: 0.9996 - top-10-accuracy: 0.9999 - top-100-accuracy: 1.0000 - val_loss: 0.0375 - val_accuracy: 0.9890 - val_top-5-accuracy: 0.9996 - val_top-10-accuracy: 0.9999 - val_top-100-accuracy: 1.0000
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Epoch 25/25
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Epoch 00025: val_accuracy did not improve from 0.98899
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11046/11046 [==============================] - 3424s 310ms/step - loss: 0.0293 - accuracy: 0.9918 - top-5-accuracy: 0.9997 - top-10-accuracy: 0.9999 - top-100-accuracy: 1.0000 - val_loss: 0.0413 - val_accuracy: 0.9876 - val_top-5-accuracy: 0.9996 - val_top-10-accuracy: 0.9999 - val_top-100-accuracy: 1.0000
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614/614 [==============================] - 54s 70ms/step - loss: 0.0374 - accuracy: 0.9892 - top-5-accuracy: 0.9996 - top-10-accuracy: 0.9999 - top-100-accuracy: 1.0000
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Accuracy on the test set: 98.92%.
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Top 5 Accuracy on the test set: 99.96%.
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Top 10 Accuracy on the test set: 99.99%.
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Top 100 Accuracy on the test set: 100.0%.
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