I am working on a CNN that deals with super-resolution. It is required that I extract patches from the image, then train on these small patches (ie.41x41).
However, when it comes to predicting the image, the image is of a larger size than the patches. But Keras doesn't allow me to predict an image of larger size than the training images.
I have read Can Keras deal with input images with different size?. I have tried the way by putting None in my network input shape and then loading the weights. However, when it comes to this line: c1 = PReLU()(c1), I get the error: nt() argument must be a string, a bytes-like object or a number, not 'NoneType'. The code is attched below.
How can I fix this problem? I am using Keras with tensorflow backend. I have no fully connected layers, all are Conv2D with relu, except for the snippet below, is PReLU for c1.
Thanks.
input_shape = (None,None,1)
x = Input(shape = input_shape)
c1 = Convolution2D(64, (3,3), init = 'he_normal', padding='same', name='Conv1')(x)
c1 = PReLU()(c1)
#............................
output_img = keras.layers.add([x, finalconv])
model = Model(x, output_img)
Keras doesn't allow me to predict an image of larger size than the
training images
This is wrong, and keras allows you to do so when your network is designed properly.
However, when it comes to this line: c1 = PReLU()(c1), I get the
error: nt() argument must be a string, a bytes-like object or a
number, not 'NoneType'.
This error is expected because your input shape contains None. Actually, if you previously set shared_axes=[1,2] for PReLU (default value shared_axes=None), you will not see this error.
Therefore, the real issue here is that PReLU's parameters, previously set only for an 41x41 input, but now are asked to work for an arbitrary input size.
The best solution is to train a new model with input shape = (None,None,3) directly.
If you don't care about the possible degradation, you can load all layer weights of your pretrained model except for the PReLU layer. Then manually compute appropriate PReLU parameters can be shared across shared_axes =[1,2], and use it as the new PReLU parameters.
Related
I am trying to add data augmentation as a layer to a model but I am getting the following error.
TypeError: The added layer must be an instance of class Layer. Found: <tensorflow.python.keras.preprocessing.image.ImageDataGenerator object at 0x7f8c2dea0710>
data_augmentation = tf.keras.preprocessing.image.ImageDataGenerator(
rotation_range=30, horizontal_flip=True)
model = Sequential()
model.add(data_augmentation)
model.add(Dense(1028,input_shape=(final_features.shape[1],)))
model.add(Dropout(0.7,input_shape=(final_features.shape[1],)))
model.add(Dense(n_classes, activation= 'softmax', kernel_regularizer='l2'))
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(final_features, y,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2,
callbacks=[lrr,EarlyStop])
I have also tried this way:
data_augmentation = Sequential(
[
preprocessing.RandomFlip("horizontal"),
preprocessing.RandomRotation(0.1),
preprocessing.RandomZoom(0.1),
]
)
model = Sequential()
model.add(data_augmentation)
model.add(Dense(1028,input_shape=(final_features.shape[1],)))
model.add(Dropout(0.7,input_shape=(final_features.shape[1],)))
model.add(Dense(n_classes, activation= 'softmax', kernel_regularizer='l2'))
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(final_features, y,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2,
callbacks=[lrr,EarlyStop])
It gives an error:
ValueError: Input 0 of layer sequential_7 is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [128, 14272]
Could you please advice how I can use augmentation in Keras?
In your first case, you are using ImageDataGenerator as a layer, which is not: as the name says, it is just a generator which applies random transformations to images (image augmentation) before feeding the network. So, the images are augmented in CPU and then feed to the neural network which can run in GPU if you have one.
Generators are usually used also to avoid loading huge datasets into memory since they allow to load only the batches being used soon.
In the second case, you are using image augmentation as layers of your model properly. The difference here is that the augmentation is run as part of your model, so if you have a GPU available for instance, those operations will run in GPU.
The problem with your second case is in the model itself (in fact the model is also wrong in the first approach, you only get an error there with the bad usage of ImageDataGenerator before your execution arrives to the model).
Note that you are using images as inputs, so, the input should be of shape (height, width, channels), but then you are starting your model with a dense layer, which expects a single array of shape (n_features,).
If your model needs to start with a Dense layer (strange, but may be ok in some case) then you need first to use Flatten layer to convert images of shape (h,w,c) into vectors of shape (h*w*c,). This change will solve your second approach for sure.
That said, you don't need to specify the input shape on every single layer: doing it in your first layer should be enough.
Last, but not least: are you sure this model is being feed with images? According to your fit call, it looks like you are using previously extracted features that may be vectors (this make sense with your current model architecture but makes no sense with the usage of image augmentation).
Please, provide more details with respect to your data to clarify this point.
I am following the self attention in Keras in the following link: How to add attention layer to a Bi-LSTM
I am new to python , what does the shape=(input_shape[-1],1) in self.add_weight and shape=(input_shape[1],1) in bias means?
The shape argument sets the expected input dimensions which the model will be fed. In your case, it is just going to be whatever the last dimension of the input shape is for the weight layer and the second dimension of the input shape for the bias layer.
Neural networks take in inputs of fixed size so while building a model, it is important that you hard code the input dimensions for each layer.
I have a question regarding the implementation of a custom loss-function for my neural network.
I am currently trying to segment cells for a project and I decided to use a unet as it seems to work quite well. In order to improve my current model, I decided to follow the idea of the original paper of the unet (https://arxiv.org/abs/1505.04597) where they implemented a weight-map assigning thus more weight to pixels that are located in between cells that are tightly associated, as you can see in this picture: Example of a weight map.
I am currently using Keras for my unet and my problem is that I do not know how to give my weights to my model without creating any problem. My idea was to create a generator with the images and a 2-channeled array containing the labels in the first channel and the weights in the second channel, that way I can extract my weights and my labels easily in my custom loss function.
My code looks like that:
train_generator = zip(image_generator, label_generator, weight_generator)
for (img, label, weight) in train_generator:
img, label = adjustData(img, True, label)
label_weights = np.concatenate((label, weight),axis=3)
# This is the final generator
yield (img, label_weights)
As you can see, I construct the train_generator with three previously constructed generators, I adjust some things and then I yield my images and combined labels and weights.
Then, when I try to fit my model with fit_generator, I get this error: ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays.
I really do not know what to do and how to implement correctly what I want to do.
Thank you in advance for your answers.
I am trying to learn to use the Keras Model API for modifying a trained model for the purpose of fine-tuning it on the go:
A very basic model:
inputs = Input((x_train.shape[1:]))
x = BatchNormalization(axis=1)(inputs)
x = Flatten()(x)
outputs = Dense(10, activation='softmax')(x)
model1 = Model(inputs, outputs)
model1.compile(optimizer=Adam(lr=1e-5), loss='categorical_crossentropy', metrics=['categorical_accuracy'])
The architecture of it is
InputLayer -> BatchNormalization -> Flatten -> Dense
After I do some training batches on it I want to add some extra Dense layer between the Flatten one and the outputs:
x = Dense(32,activation='relu')(model1.layers[-2].output)
outputs = model1.layers[-1](x)
However, when I run it, i get this:
ValueError: Input 0 is incompatible with layer dense_1: expected axis -1 of input shape to have value 784 but got shape (None, 32)
Could someone please explain what is going on and how/if can I add layers to an already trained model?
Thank you
A Dense layer is made strictly for a certain input dimension. That dimension cannot be changed after you define it (it would need a different number of weights).
So, if you really want to add layers before a dense layer that is already used, you need to make sure that the outputs of the last new layer is the same shape as the flatten's output. (It says you need 784, so your new last dense layer needs 784 units).
Another approach
Since you're adding intermediate layers, it's pointless to keep the last layer: it was trained specifically for a certain input, if you change the input, then you need to train it again.
Well... since you need to train it again anyway, why keep it? Just create a new one that will be suited to the shapes of your new previous layers.
I am working on the following keras convolutional neural network tutorial https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d
After training the model I want to test the model on sample images, and also label the images. I realize that I have to use predict method that generates an array that shows which label gets what score for a particular image. But I am having trouble using this method. If the images are in the folder test_images and there are 20 of them, how do I test these images and get the prediction?
This is how I've gotten with one image (even though I want it for multiple images):
image = cv2.imread('test1.jpg')
image = cv2.resize(image,(224,224))
features = np.swapaxes(np.swapaxes(image, 1, 2), 0, 1)
predictions = model.predict(features)
This throws the following error:
ValueError: Error when checking : expected conv2d_1_input to have 4 dimensions, but got array with shape (3, 224, 224)
Thank you very much!
Some of the questions I consulted before:
Simple Neural Network in Python not displaying label for the test image
https://github.com/fchollet/keras/issues/315
model.predict works by processing an array of samples, not just one image, so you are missing the batch/samples dimension, which in your case would only be just one image. You just have to reshape the array:
features = features.reshape((1, 3, 224, 224)
And then pass it to predict.