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SpecSeg_summary.txt
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SpecSeg_summary.txt
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Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 128, 128, 1 0 []
)]
conv2d (Conv2D) (None, 128, 128, 16 160 ['input_1[0][0]']
)
dropout (Dropout) (None, 128, 128, 16 0 ['conv2d[0][0]']
)
conv2d_1 (Conv2D) (None, 128, 128, 16 2320 ['dropout[0][0]']
)
batch_normalization (BatchNorm (None, 128, 128, 16 64 ['conv2d_1[0][0]']
alization) )
max_pooling2d (MaxPooling2D) (None, 64, 64, 16) 0 ['batch_normalization[0][0]']
conv2d_2 (Conv2D) (None, 64, 64, 32) 4640 ['max_pooling2d[0][0]']
dropout_1 (Dropout) (None, 64, 64, 32) 0 ['conv2d_2[0][0]']
conv2d_3 (Conv2D) (None, 64, 64, 32) 9248 ['dropout_1[0][0]']
batch_normalization_1 (BatchNo (None, 64, 64, 32) 128 ['conv2d_3[0][0]']
rmalization)
max_pooling2d_1 (MaxPooling2D) (None, 32, 32, 32) 0 ['batch_normalization_1[0][0]']
conv2d_4 (Conv2D) (None, 32, 32, 64) 18496 ['max_pooling2d_1[0][0]']
dropout_2 (Dropout) (None, 32, 32, 64) 0 ['conv2d_4[0][0]']
conv2d_5 (Conv2D) (None, 32, 32, 64) 36928 ['dropout_2[0][0]']
batch_normalization_2 (BatchNo (None, 32, 32, 64) 256 ['conv2d_5[0][0]']
rmalization)
max_pooling2d_2 (MaxPooling2D) (None, 16, 16, 64) 0 ['batch_normalization_2[0][0]']
conv2d_6 (Conv2D) (None, 16, 16, 128) 73856 ['max_pooling2d_2[0][0]']
dropout_3 (Dropout) (None, 16, 16, 128) 0 ['conv2d_6[0][0]']
conv2d_7 (Conv2D) (None, 16, 16, 128) 147584 ['dropout_3[0][0]']
batch_normalization_3 (BatchNo (None, 16, 16, 128) 512 ['conv2d_7[0][0]']
rmalization)
max_pooling2d_3 (MaxPooling2D) (None, 8, 8, 128) 0 ['batch_normalization_3[0][0]']
conv2d_8 (Conv2D) (None, 8, 8, 256) 295168 ['max_pooling2d_3[0][0]']
dropout_4 (Dropout) (None, 8, 8, 256) 0 ['conv2d_8[0][0]']
conv2d_9 (Conv2D) (None, 8, 8, 256) 590080 ['dropout_4[0][0]']
batch_normalization_4 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_9[0][0]']
rmalization)
conv2d_transpose (Conv2DTransp (None, 16, 16, 128) 131200 ['batch_normalization_4[0][0]']
ose)
concatenate (Concatenate) (None, 16, 16, 256) 0 ['conv2d_transpose[0][0]',
'batch_normalization_3[0][0]']
conv2d_10 (Conv2D) (None, 16, 16, 128) 295040 ['concatenate[0][0]']
dropout_5 (Dropout) (None, 16, 16, 128) 0 ['conv2d_10[0][0]']
conv2d_11 (Conv2D) (None, 16, 16, 128) 147584 ['dropout_5[0][0]']
conv2d_transpose_1 (Conv2DTran (None, 32, 32, 64) 32832 ['conv2d_11[0][0]']
spose)
concatenate_1 (Concatenate) (None, 32, 32, 128) 0 ['conv2d_transpose_1[0][0]',
'batch_normalization_2[0][0]']
conv2d_12 (Conv2D) (None, 32, 32, 64) 73792 ['concatenate_1[0][0]']
dropout_6 (Dropout) (None, 32, 32, 64) 0 ['conv2d_12[0][0]']
conv2d_13 (Conv2D) (None, 32, 32, 64) 36928 ['dropout_6[0][0]']
conv2d_transpose_2 (Conv2DTran (None, 64, 64, 32) 8224 ['conv2d_13[0][0]']
spose)
concatenate_2 (Concatenate) (None, 64, 64, 64) 0 ['conv2d_transpose_2[0][0]',
'batch_normalization_1[0][0]']
conv2d_14 (Conv2D) (None, 64, 64, 32) 18464 ['concatenate_2[0][0]']
dropout_7 (Dropout) (None, 64, 64, 32) 0 ['conv2d_14[0][0]']
conv2d_15 (Conv2D) (None, 64, 64, 32) 9248 ['dropout_7[0][0]']
conv2d_transpose_3 (Conv2DTran (None, 128, 128, 16 2064 ['conv2d_15[0][0]']
spose) )
concatenate_3 (Concatenate) (None, 128, 128, 32 0 ['conv2d_transpose_3[0][0]',
) 'batch_normalization[0][0]']
conv2d_16 (Conv2D) (None, 128, 128, 16 4624 ['concatenate_3[0][0]']
)
dropout_8 (Dropout) (None, 128, 128, 16 0 ['conv2d_16[0][0]']
)
conv2d_17 (Conv2D) (None, 128, 128, 16 2320 ['dropout_8[0][0]']
)
conv2d_18 (Conv2D) (None, 128, 128, 1) 17 ['conv2d_17[0][0]']
==================================================================================================
Total params: 1,942,801
Trainable params: 1,941,809
Non-trainable params: 992
__________________________________________________________________________________________________