I got an Xception model. I have combined the model to change the Input Channel to 3. however I have got error Answer You simply have to embed Xception in the correct way in your new model: We create a new Input layer, than we operate upsampling and in the end we pass all to Xception Here is the running
Tag: deep-learning
Using densenet with fastai
I am trying to train a densenet model using the fast.ai library. I checked the documentation and I managed to make it work for resnet50. However, for densenet, it seems to be unable to find the module. I tried to use arch=models.dn121 as stated by this forum. But I get the same error. Can anyone please help? Here is the
Keras flatten: ValueError: Attempt to convert a value (None) with an unsupported type () to a Tensor
I have the error mentioned in the title, with the following code This sends the following error According to the question asked with the same error it happens when you mix up keras and tf.keras. But i think have defined the imports accordingly, so unless there is a clash between imports or a bad definition of them i do not
In a convolutional neural network, how do I use Maxout instead of ReLU as an activation function?
How do I use Maxout instead of’relu’ for activation? Answer You can use tensorflow_addons.layers.Maxout to add Maxout Activation function You can install tensorflow_addons by:
ValueError: not enough values to unpack (expected 3, got 2) in Pytorch
this is my Define validate function when I load the model and start prediction using this code I have received the error using PyTorch.and after this, I am iterating through the epoch loop and batch loop and I landed with this error. And this is the main function where I call validate function get the error when model is loaded
Last layer in a RNN – Dense, LSTM, GRU…?
I know you can use different types of layers in an RNN architecture in Keras, depending on the type of problem you have. What I’m referring to is for example layers.SimpleRNN, layers.LSTM or layers.GRU. So let’s say we have (with the functional API in Keras): Where lstm_3 is the last layer. Does it make sense to have it as an
Pytorch getting RuntimeError: Found dtype Double but expected Float
I am trying to implement a neural net in PyTorch but it doesn’t seem to work. The problem seems to be in the training loop. I’ve spend several hours into this but can’t get it right. Please help, thanks. I haven’t added the data preprocessing parts. (tensor([ 5., 5., 8., 14.], dtype=torch.float64), tensor(-0.3403, dtype=torch.float64)) Error: Answer You need the data
I have a data type problem in the text classification problem
I want to build deep learning classifiers for Kickstarter campaign prediction. I have a problem with the part of the model but I can not solve this. My code: In this point, I am getting ValueError: Failed to find data adapter that can handle input: <class ‘scipy.sparse.csr.csr_matrix’>, (<class ‘list’> containing values of types {“<class ‘str’>”}) I try np.asarray for solving
How to remove first N layers from a Keras Model?
I would like to remove the first N layers from the pretrained Keras model. For example, an EfficientNetB0, whose first 3 layers are responsible only for preprocessing: As M.Innat mentioned, the first layer is an Input Layer, which should be either spared or re-attached. I would like to remove those layers, but simple approach like this throws error: This will
How to add two separate layers on the top of one layer using pytorch?
I want to add two separate layers on the top of one layer (or a pre-trained model) Is that possible for me to do using Pytorch? Answer Yes, when defining your model’s forward function, you can specify how the inputs should be passed through the layers. For example: Where forward is a member of MyNet: Training The model should be