conv-neural-network

i made a convolutional nuralnetwork and i want it to take input pictures and output pictures but when i turn the pictures into tensors they have the wrong dimension : RuntimeError: Expected 4-dimensional input for 4-dimensional weight [20, 3, 5, 5], but got 3-dimensional input of size [900, 1440, 3] instead how do i change the dimension of the pictures ? and why does it need to be changed? and how do i make the output an picture? i tryed to use transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) to normilize the img but it didnt

I have built a model to train a convolutional autoencoder in TensorFlow. I followed the instructions on Reading Data from the TF documentation to read in my own images of size 233 x 233 x 3. Here is my convert_to() function adapted from those instructions: def convert_to(images, name): """Converts a dataset to tfrecords.""" num_examples = images.shape[0] rows = images.shape[1] cols = images.shape[2] depth = images.shape[3] filename = os.path.join(FLAGS.tmp_dir, name + '.tfrecords') print('Writing', filename) writer = tf.python_io.TFRecordWriter(filename) for index in range(num_examples): print

I have a lot of PNG images that I want to classify, using a trained CNN model. To speed up the process, I would like to use multiple-processing with CPUs (I have 72 available, here I'm just using 4). I don't have a GPU available at the moment, but if necessary, I could get one. My workflow: read a figure with openCV adapt shape and format use mymodel.predict(img) to get the probability for each class When it comes to the prediction step, it never finishes the mymodel.predict(img) step. When I use the code without the multiprocessing module, it works fine. For the model, I'm using keras with a

I am new to machine learning. I got a task to find the total number of vehicles from an image using machine learning concept. I am using neural network. My image of worst case is given here. Traffic Image I need to find the total number of cars from this image. My idea is to cut this big image into small patches of image and train the network to count the vehicles from the small patches. Each patch will be having count less than 5. Then in the processing of new image, I could make use of a sliding window to get the total count of vehicles. I just want to know whether this idea is possible or

I need to do realtime augmentation on my dataset for input to CNN, but i am having a really tough time finding suitable libraries for it. I have tried caffe but the DataTransform doesn't support many realtime augmentations like rotating etc. So for ease of implementation i settled with Lasagne . But it seems that it also doesn't support realtime augmentation. I have seen some posts related to Facial Keypoints detection where he's using Batchiterator of nolearn.lasagne . But i am not sure whether its realtime or not. There's no proper tutorial for it. So finally how should i do realtime

## what my model looks like # defining the model archictecture model = Sequential() # 1st conv layer model.add(Conv2D(32, (5, 5), activation='relu', input_shape=x_ip_shape)) # 1st max pool model.add(MaxPooling2D(pool_size=(2, 2))) # 2nd conv layer model.add(Conv2D(64, (7, 7), activation='relu')) # 2nd max pool model.add(MaxPooling2D(pool_size=(2, 2))) # Flattenning the input model.add(Flatten()) # 1st Fully connected layer model.add(Dense(10, activation='relu')) # Adding droput model.add(Dropout(0.25)) # softmax layer model.add(Dense(classes_out, activation='softmax')) # defining loss,