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shape of an output tensor after convolutional filter on a colour image

I find it difficult to understand a notion about tensors.
For VGG (https://www.tensorflow.org/api_docs/python/tf/keras/applications/VGG16), we start from a batch of colour images (none,224,224,3) and apply 64 2D convolutional filters.
At the output we obtain a tensor of (none,224,224,64), we can see this by making a summary of the model.
However, a filter must treat all 3 colours and my intuition tells me that I should have an output tensor of (none,224,224,3,64).
Could one explain to me why my reasoning is wrong?
Thank you for your explanations.

All filters have shape
(kernel_height, kernel_width, input_channels)
When they pass on your input data with 'SAME' padding, the output shape result is
(input_height, input_width)
And that, for all filters, so
(input_height, input_width, n_filters)

Can 2D convolutional neural network be converted into 1D convolutional neural network?

I have designed a neural network using 2d convolutional layers and max-pooling layers with the input shape for input, one hot encoded sequences as 2d array. then it is reshaped before inputting the model.
data = np.zeros( (100, 21 * 1000), dtype=np.float32 )
#reshape
x_data = tf.reshape( data, [-1, 1, 1000, 21] )
However, I used the same dataset using 1D convolutional layers by changing the model and input array without reshaping as it is 1D
data = np.zeros( (100, 1000,21), dtype=np.float32 )
finally, the 1D convolutional model performed well with 96% act. and 2d CNN gave 93%. Can someone explain to me what actually happens there to increase the accuracy?

Can someone explain to me what actually happens there to increase the accuracy?
That's hard to tell and depends on your specific dataset, network, hyperparameters etc.
Generally, in a conv2D-Layer the filter shifts horizontal and vertical. In a conv1D-Layer the filter shifts only vertical in the convolution process.
So which one is the best? That depends on your problem. For time series conv1D could be better and for images conv2D could be the better choice.

Should I use only "exactly same" input shape for transfer learning?

I trained a CNN model with (5x128x128x3) size of input shape
and I got trained weight of (5x128x128x3)
by the way, I wanna use this weight for training (7x128x128x3) size of input data
So, this is my question
should I use only same shape of input?
I wonder if I can use another size (in this case, 7x128x128x3) of input for transfer learning
ValueError: Error when checking input: expected input_1 to have shape (5, 128, 128, 3) but got arry with shape (7, 128, 128, 3)```

Let's break down the dimensions (5x128x128x3):
The first dimension is the batch size (which was 5 when the original model was trained). This is irrelevant and you can set it to None as pointed out in the comments to feed arbitrary sized batches to the model.
The second to third dimensions (128x128) are the width and height of the image and you may be able to change these, but it's hard to say for sure without knowing the model architecture and which layer output you're using for transfer learning. The reason you can change these is that 2d convolutional filters are repeated across the 2d dimensions (width and height) of the image, so they will remain valid for different widths and heights (assuming compatible padding). But if you change the 2d dimensions too much, it is possible that the receptive fields of the layers are changed in a way that hurts transfer learning performance. Eg. if the 7th conv layer in the network for 128x128 input can see the entire input image in each activation (a receptive field of 128x128), then if you double the width and height, it won't anymore and the layer may not recognize certain global features.
The fourth dimension is the number of channels in the input images and you can't change this, as the filters in the first layer will have 3 weights across the depth dimension.

Transfer learning with Keras: Input shape mismatch

I'm running a classification and predition neural network algorithme using pre-trained model with keras.
Now I know the shape of the input for keras is (224,224,3) but my input has this shape (180, 200, 20) and I get the following error:
ValueError: Dimension 0 in both shapes must be equal, but are 3 and 64. Shapes are [3,3,20,64] and [64,3,3,3]. for 'Assign_32' (op: 'Assign') with input shapes: [3,3,20,64], [64,3,3,3].
and here is the code:
from keras import applications
from keras.layers import Input
input_tensor = Input(shape = (180, 200, 20))
vgg_model = applications.VGG16(weights = 'imagenet', include_top = False, input_tensor = input_tensor)
vgg_model.summary()
Any idea how to get around this? Thank you

From Documentation:
input_shape: optional shape tuple, only to be specified if include_top
is False (otherwise the input shape has to be (224, 224, 3) (with
'channels_last' data format) or (3, 224, 224) (with 'channels_first'
data format). It should have exactly 3 inputs channels, and width and
height should be no smaller than 32. E.g. (200, 200, 3) would be one
valid value.
You can try to create a vgg16 from scratch from this link. VGG16 model for Keras

You need to resize your input image
from keras.preprocessing import image
img = image.load_img("image1.jpeg",target_size=(224,224))
If you want to learn to do transfer learning from scratch in keras you can read this article. This article has step by step implementation.
https://medium.com/#1297rohit/transfer-learning-from-scratch-using-keras-339834b153b9

In your case, since you are not dealing with images of the right size (or number of channels) you may want to cut out large parts of the vgg network to still save the information contained in the middle layers, but I am not sure how efficient it would be.
You would need to remove the first convolution layer, and all the dense layers at the end, replacing them with your own layers. You would certainly need to retrain the whole network, so rather than transfer learning you would be doing very smart initialization.

what is the difference between Conventional 1D or 2D in keras layers

I'm discovering keras library and i can't tell what does the dimention mean in keras layers and how to choose them ? (model.add(Convolution2D(...)) or model.add(Convolution1D(...)) ).
For example i have a set of 9000 train traces and 1000 of test traces and each trace has 1000 samples, so i created the arrays X_train with a size of 9000*1000, X_test has a size of 1000*1000, Y_train has a size of 9000, and Y_test has a size of 1000.
my question is how can i choose the first layer dimension ?.
I tried using the same example implemented in MNIST such :
model.add(Convolution2D(9000, (1, 1), activation='relu', input_shape(1,9000000,1),dim_ordering='th'))
but it didn't work, i don't even know what should i put in each argument of Convolution function.

The choice of dimension (1D, 2D, etc.) depends on the dimensions of your input. For example, since you're using the MNIST dataset, you would use 2D layers since your input is an image with height and width (two dimensions). Alternatively, if you were using text data, you might use a 1D layer because sentences are linear lists of words (one dimension).
I would suggest looking at Francois Chollet's example of a convolutional neural net with MNIST: https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py. (Note: Conv2D is the same as Convolution2D.)