Unfolding Keras model summary for two joint sequential models
Solution 1:
There's a parameter called expand_nested=True from tf 2.7 for a model summary method that will disclose inner nested loop layers (issue, pr). But as you're using a relatively older version, tf 2.4, you can adopt my following workaround,
def summary_plus(layer, i=0):
if hasattr(layer, 'layers'):
if i != 0:
layer.summary()
for l in layer.layers:
i += 1
summary_plus(l, i=i)
summary_plus(model) # OK
Solution 2:
I would suggest trying to set the expanded_nested parameter of model.summary() to True, which will expand the nested models as stated in the docs (Does not exist in older TF versions). It is not the prettiest output, but it does the job:
print(model.summary(expand_nested=True))
Model: "sequential_5"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
sequential_3 (Sequential) (None, 2048) 14714688
|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|
| conv2d_13 (Conv2D) (None, 64, 64, 64) 1792 |
| |
| conv2d_14 (Conv2D) (None, 64, 64, 64) 36928 |
| |
| max_pooling2d_2 (MaxPooling (None, 32, 32, 64) 0 |
| 2D) |
| |
| conv2d_15 (Conv2D) (None, 32, 32, 128) 73856 |
| |
| conv2d_16 (Conv2D) (None, 32, 32, 128) 147584 |
| |
| max_pooling2d_3 (MaxPooling (None, 16, 16, 128) 0 |
| 2D) |
| |
| conv2d_17 (Conv2D) (None, 16, 16, 256) 295168 |
| |
| conv2d_18 (Conv2D) (None, 16, 16, 256) 590080 |
| |
| conv2d_19 (Conv2D) (None, 16, 16, 256) 590080 |
| |
| block3_pool (MaxPooling2D) (None, 8, 8, 256) 0 |
| |
| conv2d_20 (Conv2D) (None, 8, 8, 512) 1180160 |
| |
| conv2d_21 (Conv2D) (None, 8, 8, 512) 2359808 |
| |
| conv2d_22 (Conv2D) (None, 8, 8, 512) 2359808 |
| |
| block4_pool (MaxPooling2D) (None, 4, 4, 512) 0 |
| |
| conv2d_23 (Conv2D) (None, 4, 4, 512) 2359808 |
| |
| conv2d_24 (Conv2D) (None, 4, 4, 512) 2359808 |
| |
| conv2d_25 (Conv2D) (None, 4, 4, 512) 2359808 |
| |
| block5_pool (MaxPooling2D) (None, 2, 2, 512) 0 |
| |
| flatten (Flatten) (None, 2048) 0 |
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sequential_4 (Sequential) (None, 64, 64, 3) 34715075
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| dense_1 (Dense) (None, 16384) 33570816 |
| |
| batch_normalization_4 (Batc (None, 16384) 65536 |
| hNormalization) |
| |
| activation_5 (Activation) (None, 16384) 0 |
| |
| reshape_1 (Reshape) (None, 8, 8, 256) 0 |
| |
| dropout_1 (Dropout) (None, 8, 8, 256) 0 |
| |
| up_sampling2d_3 (UpSampling (None, 16, 16, 256) 0 |
| 2D) |
| |
| conv2d_transpose_4 (Conv2DT (None, 16, 16, 128) 819328 |
| ranspose) |
| |
| batch_normalization_5 (Batc (None, 16, 16, 128) 512 |
| hNormalization) |
| |
| activation_6 (Activation) (None, 16, 16, 128) 0 |
| |
| up_sampling2d_4 (UpSampling (None, 32, 32, 128) 0 |
| 2D) |
| |
| conv2d_transpose_5 (Conv2DT (None, 32, 32, 64) 204864 |
| ranspose) |
| |
| batch_normalization_6 (Batc (None, 32, 32, 64) 256 |
| hNormalization) |
| |
| activation_7 (Activation) (None, 32, 32, 64) 0 |
| |
| conv2d_transpose_6 (Conv2DT (None, 32, 32, 32) 51232 |
| ranspose) |
| |
| batch_normalization_7 (Batc (None, 32, 32, 32) 128 |
| hNormalization) |
| |
| activation_8 (Activation) (None, 32, 32, 32) 0 |
| |
| up_sampling2d_5 (UpSampling (None, 64, 64, 32) 0 |
| 2D) |
| |
| conv2d_transpose_7 (Conv2DT (None, 64, 64, 3) 2403 |
| ranspose) |
| |
| activation_9 (Activation) (None, 64, 64, 3) 0 |
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=================================================================
Total params: 49,429,763
Trainable params: 49,396,547
Non-trainable params: 33,216
_________________________________________________________________
None
For older TF versions, just run print(model.layers[0].summary()) and print(model.layers[1].summary()).