I have trained my MobileNetV3Small with 2 dense layers and got my result. but I set 640,480,3 as my input shape and now I need to test some images with different sizes. as it starts with convolution layers size must not matter. but I received errors as it requires its defined size (640,480,3) and padding and resizing didn't perform well. so I want to change the input shape to (None, None,3) without retraining it from scratch. It took 3 days to train my model and I don't want to train it again to change the input size.
Maybe you could try this on your Keras.Model instance:
model.input.set_shape((None, None, 3))
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I am using Keras to build a CNN to work with the CIFAR-10 dataset. I am slightly confused at one of the last lines of an online tutorial. They take 50,000 32x32 color images and process them through 4 convolutional layers and one fully connected layer. The last part is accomplished by:
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
I am trying to understand why it is model.add(Dense(512)) and not some other number. For example, I thought 32x32 images can be flattened to a 1024-size vector. But, why did they choose 512 here?
Thanks!
Actualy not 32x32, it's 32x32x3 because of color channels and flatten and dense different methods I think you won't get the code there is low level implementation:
W1=tf.Variable(tf.random_normal([32*32*3,512]),name="W1") #variable
x=tf.placeholder(tf.float32,[batch,32,32,3]) #placeholder for inputs
flat=tf.reshape(x,[batch,32*32*3]) #model.add(Flatten())
mul1=tf.matmul(flat,W1) #model.add(Dense(512))
relu=tf.nn.relu(mul1) #model.add(Activation('relu'))
flat's shape=[batch,32*32*3]
mul1's shape=[batch,512]
Of course it could be 1024 or 5000 but it becomes harder to optimize.
I'm searching for a data leak in my model. I'm using tf.layers.dense before a masking operation and am concerned that the model could just learn to switch positions in the middle dimension of my input tensor.
When I have an input tensor x = tf.ones((2,3,4)) would tf.layers.dense(x,8) flatten x to a fully connected layer with 2*3*4=24 input neurons and 2*3*8=48 output neurons then reshape it again to [2,3,8], or would it create 2*3=6 fully connected layers with 4 input and 8 output neurons then concatenate them?
As for the Keras Dense layer, it has been already mentioned in another answer that its input is not flattened and instead, it is applied on the last axis of its input.
As for the TensorFlow Dense layer, it is actually inherited from Keras Dense layer and as a result, same as Keras Dense layer, it is applied on the last axis of its input.
I would like to fine tune the VGG16 model using my own grayscale images. I know I can fine tune/add my own top layers by doing something like:
base_model = keras.applications.vgg16.VGG16(include_top=False, weights='imagenet', input_tensor=None, input_shape=(im_height,im_width,channels))
but only when channels = 3 according to the documentation.
I have thought of simply adding two redundant channels to my image, but this seems like a waste of computation/could make the classification worse. I could also replicate the same image across three channels, but I am similarly unsure of how it would preform.
Keras pre-trained models have trained on color images and if you want to use their full power, you should use color images for fine-tuning. However, if you have grayscale images you can still use these pre-trained models by repeating your grayscale image over three channels. But obviously, it will not as well as using color images as input.
The VGG keras model uses the function: keras.applications.imagenet_utils._obtain_input_shape.
This function was tailored for ImageNet data thus it enforces the input channel to be 3. One possible workaround will be to copy the VGG16 module and replace the line:
input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=48, data_format=K.image_data_format(), include_top=include_top)
with:
input_shape = (im_height, im_width, 1)
As a side note, you will not be able to load ImageNet weights since your input space has changed and the first layer convolutions will not match.
I am trying to experiment with a multi-layer encoder-decoder type of network. The screenshot of the last several layers of network architecture is as follows. This is how I setup model compiling and training process.
optimizer = SGD(lr=0.001, momentum=0.9, decay=0.0005, nesterov=False)
autoencoder.compile(loss="sparse_categorical_crossentropy", optimizer=optimizer, metrics=['accuracy'])
model.fit(imgs_train, imgs_mask_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1,callbacks=[model_checkpoint])
imgs_train and imgs_mask_train are of shape (2000, 1, 128, 128). imgs_train represent the raw image and imgs_mask_train represents the mask image. I am trying to solve a semantic segmentation problem. However, running the program generates the following error message, (I only keep the main related part).
tensorflow.python.pywrap_tensorflow.StatusNotOK: Invalid argument: logits first dimension must match labels size. logits shape=[4096,128] labels shape=[524288]
[[Node: SparseSoftmaxCrossEntropyWithLogits = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](Reshape_364, Cast_158)]]
It seems to me that the loss function of sparse_categorical_crossentropy causes the problem for the current (imgs_train, imgs_mask_train) shape setting. The Keras API does not include the detail about how to setup the target tensor. Any suggestions are highly appreciated!
I am currently trying to figure the same problem and as far as I can tell it takes a sparse representation of the target category. That means integers as the target label instead of the one-hot encoded binary class matrix.
Concerning your problem, do you have categories in your masking or do you just have information about the outline of an object? With outline information it becomes a pixel wise binary loss instead of a categorical one. If you have categories, the output of your decoder should have dimensionality (None, number_of_classes, 128, 128). On that you should be able to use a sparse target mask but I haven't tried this myself...
Hope that helps
Is it possible to train a Tensorflow Inception V3 model with an image size greater than size 299x299? Seems that the Inception V3 CNN is designed for this image size only.
As long as you do not include the fully connected (Dense) layers at the top, it should be fine to use a different image size.
You can do that by adding this argument while loading the model
base_model = InceptionV3(weights=weights, include_top=False)
The convolutional layer weights should be independent of the image size in general and hence you can use those weights. The FC layer of the pre-trained network with n fully connected nodes would have a weight matrix of size[m X n]. This layer will expect the input to that layer to be of size m. However, due to the change in image size, you will end up with a different value for m when you feed the image from the new dataset(convolution filter convolving on a different image size).
After adding new dense layers, you can fine-tune the network to train on the top layers alone (keeping the weights of the conv-blocks below it fixed).