I am trying to use the Masking layer with ConvLSTM2D in keras. However, I keep gettin the error:
ValueError: Shape must be rank 4 but is rank 2 for 'conv_lst_m2d_1/while/Tile' (op: 'Tile') with input shapes: [?,128,128,1], [2].
My input shape is (None, 128, 128, 1)
Related
ValueError: One of the dimensions in the output is <= 0 due to downsampling in conv3d_15. Consider increasing the input size. Received input shape [None, 1, 1, 1, 1904211] which would produce output shape with a zero or negative value in a dimension.
Can anyone explain me what is the meaning of this error
While i am trying to build my 3d convolutional neural network. I got this erro
A 3D convolution layer expect the input to be in a shape similar to (4, 28, 28, 28, 1). For 28x28x28 volumes with a single channel. More info here - https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv3D
Can someone please explain me the inputs and outputs along with the working of the layer mentioned below
model.add(Embedding(total_words, 64, input_length=max_sequence_len-1))
total_words = 263
max_sequence_len=11
Is 64, the number of dimensions?
And why is the output of this layer (None, 10, 64)
Shouldn't it be a 64 dimension vector for each word, i.e (None, 263, 64)
You can find all the information about the Embedding Layer of Tensorflow Here.
The first two parameters are input_dimension and output_dimension.
The input dimensions basically represents the vocabulary size of your model. You can find this out by using the word_index function of the Tokenizer() function.
The output dimensions are going to be Dimensions of the input of the next Dense Layer
The output of the Embedding layer is of the form (batch_size, input_length, output_dim). But since you specified the input_length parameter, your layers input will be of the form (batch, input_length). That's why the output is of the form (None, 10 ,64).
Hope that clears up your doubt ☺️
In the Embedding layer the first argument represents the input dimensions (which is typically of considerable dimensionality). The second argument represents the output dimensions, a.k.a the dimensionality of the reduced vector. The third argument is for the sequence length. In essence, an Embedding layer is simply learning a lookup table of shape (input dim, output dim). The weights of this layer reflect that shape. The output of the layer, however, will of course be of shape (output dim, seq length); one dimensionality-reduced embedding vector for each element in the input sequence. The shape you were expecting is actually the shape of the weights of an embedding layer.
I am having a problem with data shape when I try to feed it into my ConvLSTM network.
I already know that the train input for ConvLSTM should be 5 dimensional (number of sequences, number of samples in each sequence, rows, columns, channels). And I know that the labels for the same network should be four dimensional as it is expected from python. So my training data set has the shape (105, 15, 30, 40, 3) and for the following label data set for training I created the array of shape (105*15, 30, 40, 3).But I am getting the following error:
ValueError: Input arrays should have the same number of samples as target arrays. Found 105 input samples and 1575 target samples.
im trying to fit the data with the following shape to the pretrained keras vgg19 model.
image input shape is (32383, 96, 96, 3)
label shape is (32383, 17)
and I got this error
expected block5_pool to have 4 dimensions, but got array with shape (32383, 17)
at this line
model.fit(x = X_train, y= Y_train, validation_data=(X_valid, Y_valid),
batch_size=64,verbose=2, epochs=epochs,callbacks=callbacks,shuffle=True)
Here's how I define my model
model = VGG16(include_top=False, weights='imagenet', input_tensor=None, input_shape=(96,96,3),classes=17)
How did maxpool give me a 2d tensor but not a 4D tensor ? I'm using the original model from keras.applications.vgg16. How can I fix this error?
Your problem comes from VGG16(include_top=False,...) as this makes your solution to load only a convolutional part of VGG. This is why Keras is complaining that it got 2-dimensional output insted of 4-dimensional one (4 dimensions come from the fact that convolutional output has shape (nb_of_examples, width, height, channels)). In order to overcome this issue you need to either set include_top=True or add additional layers which will squash the convolutional part - to a 2d one (by e.g. using Flatten, GlobalMaxPooling2D, GlobalAveragePooling2D and a set of Dense layers - including a final one which should be a Dense with size of 17 and softmax activation function).
The Keras tutorial gives the following code example (with comments):
# apply a convolution 1d of length 3 to a sequence with 10 timesteps,
# with 64 output filters
model = Sequential()
model.add(Convolution1D(64, 3, border_mode='same', input_shape=(10, 32)))
# now model.output_shape == (None, 10, 64)
I am confused about the output size. Shouldn't it create 10 timesteps with a depth of 64 and a width of 32 (stride defaults to 1, no padding)? So (10,32,64) instead of (None,10,64)
In k-Dimensional convolution you will have a filters which will somehow preserve a structure of first k-dimensions and will squash the information from all other dimension by convoluting them with a filter weights. So basically every filter in your network will have a dimension (3x32) and all information from the last dimension (this one with size 32) will be squashed to a one real number with the first dimension preserved. This is the reason why you have a shape like this.
You could imagine a similar situation in 2-D case when you have a colour image. Your input will have then 3-dimensional structure (picture_length, picture_width, colour). When you apply the 2-D convolution with respect to your first two dimensions - all information about colours will be squashed by your filter and will no be preserved in your output structure. The same as here.