Keras: How to get layer shapes in a Sequential model
Question:
I would like to access the layer size of all the layers in a Sequential
Keras model. My code:
model = Sequential()
model.add(Conv2D(filters=32,
kernel_size=(3,3),
input_shape=(64,64,3)
))
model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2)))
Then I would like some code like the following to work
for layer in model.layers:
print(layer.get_shape())
.. but it doesn’t. I get the error: AttributeError: 'Conv2D' object has no attribute 'get_shape'
Answers:
Just use model.summary()
, and it will print all layers with their output shapes.
If you need them as arrays, tuples or etc, you can try:
for l in model.layers:
print (l.output_shape)
For layers that are used more than once, they contain "multiple inbound nodes", and you should get each output shape separately:
if isinstance(layer.outputs, list):
for out in layer.outputs:
print(K.int_shape(out))
It will come as a (None, 62, 62, 32) for the first layer. The None
is related to the batch_size, and will be defined during training or predicting.
If you want the output printed in a fancy way:
model.summary()
If you want the sizes in an accessible form
for layer in model.layers:
print(layer.get_output_at(0).get_shape().as_list())
There are probably better ways to access the shapes than this. Thanks to Daniel for the inspiration.
According to official doc for Keras Layer, one can access layer output/input shape via layer.output_shape
or layer.input_shape
.
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D
model = Sequential(layers=[
Conv2D(32, (3, 3), input_shape=(64, 64, 3)),
MaxPool2D(pool_size=(3, 3), strides=(2, 2))
])
for layer in model.layers:
print(layer.output_shape)
# Output
# (None, 62, 62, 32)
# (None, 30, 30, 32)
I would like to access the layer size of all the layers in a Sequential
Keras model. My code:
model = Sequential()
model.add(Conv2D(filters=32,
kernel_size=(3,3),
input_shape=(64,64,3)
))
model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2)))
Then I would like some code like the following to work
for layer in model.layers:
print(layer.get_shape())
.. but it doesn’t. I get the error: AttributeError: 'Conv2D' object has no attribute 'get_shape'
Just use model.summary()
, and it will print all layers with their output shapes.
If you need them as arrays, tuples or etc, you can try:
for l in model.layers:
print (l.output_shape)
For layers that are used more than once, they contain "multiple inbound nodes", and you should get each output shape separately:
if isinstance(layer.outputs, list):
for out in layer.outputs:
print(K.int_shape(out))
It will come as a (None, 62, 62, 32) for the first layer. The None
is related to the batch_size, and will be defined during training or predicting.
If you want the output printed in a fancy way:
model.summary()
If you want the sizes in an accessible form
for layer in model.layers:
print(layer.get_output_at(0).get_shape().as_list())
There are probably better ways to access the shapes than this. Thanks to Daniel for the inspiration.
According to official doc for Keras Layer, one can access layer output/input shape via layer.output_shape
or layer.input_shape
.
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D
model = Sequential(layers=[
Conv2D(32, (3, 3), input_shape=(64, 64, 3)),
MaxPool2D(pool_size=(3, 3), strides=(2, 2))
])
for layer in model.layers:
print(layer.output_shape)
# Output
# (None, 62, 62, 32)
# (None, 30, 30, 32)