Note: Going through the yolov3_darknet demo file in colab.
The error while trying to load the model file.
Note: .h5 file is downloaded from the given link in notebook only.
the error is as below!
/usr/local/lib/python3.10/dist-packages/keras/src/engine/keras_tensor.py in len(self)
244
245 def len(self):
--> 246 raise TypeError(
247 "Keras symbolic inputs/outputs do not "
248 "implement __len__. You may be "
TypeError: Exception encountered when calling layer "reshape" (type Reshape).
Keras symbolic inputs/outputs do not implement __len__. You may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model. This error will also get raised if you try asserting a symbolic input/output directly.
Call arguments received by layer "reshape" (type Reshape):
• inputs=tf.Tensor(shape=(None, None, None, 75), dtype=float32)
Note: Going through the yolov3_darknet demo file in colab.
The error while trying to load the model file.
Note: .h5 file is downloaded from the given link in notebook only.
the error is as below!
/usr/local/lib/python3.10/dist-packages/keras/src/engine/keras_tensor.py in len(self)
244
245 def len(self):
--> 246 raise TypeError(
247 "Keras symbolic inputs/outputs do not "
248 "implement
__len__. You may be "TypeError: Exception encountered when calling layer "reshape" (type Reshape).
Keras symbolic inputs/outputs do not implement
__len__. You may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model. This error will also get raised if you try asserting a symbolic input/output directly.Call arguments received by layer "reshape" (type Reshape):
• inputs=tf.Tensor(shape=(None, None, None, 75), dtype=float32)