The model’s architecture cannot be changed because the weights have been trained for a specific input configuration. Replacing the first layer with your own would pretty much render the rest of the weights useless. — Edit: elaboration suggested by Prune– CNNs are built so that as they go deeper, they can extract high-level features derived … Read more
you can use this formula [(W−K+2P)/S]+1. W is the input volume – in your case 128 K is the Kernel size – in your case 5 P is the padding – in your case 0 i believe S is the stride – which you have not provided. So, we input into the formula: Output_Shape = … Read more
stack Concatenates sequence of tensors along a new dimension. cat Concatenates the given sequence of seq tensors in the given dimension. So if A and B are of shape (3, 4): torch.cat([A, B], dim=0) will be of shape (6, 4) torch.stack([A, B], dim=0) will be of shape (2, 3, 4)
In keras – while building a sequential model – usually the second dimension (one after sample dimension) – is related to a time dimension. This means that if for example, your data is 5-dim with (sample, time, width, length, channel) you could apply a convolutional layer using TimeDistributed (which is applicable to 4-dim with (sample, … Read more
You get this error because your model is on the GPU, but your data is on the CPU. So, you need to send your input tensors to the GPU. inputs, labels = data # this is what you had inputs, labels = inputs.cuda(), labels.cuda() # add this line Or like this, to stay consistent with … Read more
The purpose of the activation function is to introduce non-linearity into the network in turn, this allows you to model a response variable (aka target variable, class label, or score) that varies non-linearly with its explanatory variables non-linear means that the output cannot be reproduced from a linear combination of the inputs (which is not … Read more
Here’s a nice function that converts a caffe net to a python list of dictionaries, so you can pickle it and read it anyway you want: import caffe def shai_net_to_py_readable(prototxt_filename, caffemodel_filename): net = caffe.Net(prototxt_filename, caffemodel_filename, caffe.TEST) # read the net + weights pynet_ = [] for li in xrange(len(net.layers)): # for each layer in the … Read more
The easiest way is to create a new model in Keras, without calling the backend. You’ll need the functional model API for this: from keras.models import Model XX = model.input YY = model.layers[0].output new_model = Model(XX, YY) Xaug = X_train[:9] Xresult = new_model.predict(Xaug)
You can wrap the loss function as a inner function and pass your input tensor to it (as commonly done when passing additional arguments to the loss function). def custom_loss_wrapper(input_tensor): def custom_loss(y_true, y_pred): return K.binary_crossentropy(y_true, y_pred) + K.mean(input_tensor) return custom_loss input_tensor = Input(shape=(10,)) hidden = Dense(100, activation=’relu’)(input_tensor) out = Dense(1, activation=’sigmoid’)(hidden) model = Model(input_tensor, out) … Read more
The most common way is to use either pickle or joblib. Here you have an example on how to use pickle in order to save Tokenizer: import pickle # saving with open(‘tokenizer.pickle’, ‘wb’) as handle: pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL) # loading with open(‘tokenizer.pickle’, ‘rb’) as handle: tokenizer = pickle.load(handle)