conv-neural-network

Is it possible to change the input source of ImageData layer or a MemoryData layer on the fly? I am trying to shuffle the data every epoch but I have both image and some other non-image features that I want to concatenate at a later stage in the network. I could not find a reliable way to shuffle both the image and my other data in a way that preserves the alignment of the two. So, I am thinking of re-generating the imagelist.txt as well as nonimage data (in Memory) every epoch and attach the new file to the ImageData layer and initialize MemoryDataLayer with the new data. How can I make sure

I am trying to do transfer learning for VGG16 architecture with 'ImageNet' pretrained weights on PASCAL VOC 2012 dataset. PASCAL VOC is a multi label image dataset with 20 classes, and so I have modified the inbuilt VGG16 model like this: def VGG16_modified(): base_model = vgg16.VGG16(include_top=True,weights='imagenet',input_shape=(224,224,3)) print(base_model.summary()) x = base_model.get_layer('block5_pool').output x = (GlobalAveragePooling2D())(x) predictions = Dense(20,activation='sigmoid')(x) final_model = Model(input = base_model.input, output = predictions) print(final_model.summary())

I'm building one but, and when I use the custom train function provided on lenet example with a batch size bigger than 110 my accuracy gets bigger than 1 (100%). If I use batch size 32, I get 30 percent of accuracy. Batch size equal 64 my net accuracy is 64. And batch size equal to 128, the accuracy is 1.2. My images are 32x32. Train dataset: 56 images of Neutral faces. 60 images of Surprise faces. Test dataset: 15 images of Neutral faces. 15 images of Surprise faces. This is my code: def train(solver): niter = 200 test_interval = 25 train_loss = zeros(niter) test_acc = zeros(int(np.ceil(niter

This is my code: cnn_input = Input(shape=(cnn_max_length,)) emb_output = Embedding(num_chars + 1, output_dim=32, input_length=cnn_max_length, trainable=True)(cnn_input) output = TimeDistributed(Convolution1D(filters=128, kernel_size=4, activation='relu'))(emb_output) I want to train a character-level CNN sequence labeler and I keep receiving this error: Traceback (most recent call last): File "word_lstm_char_cnn.py", line 24, in <module> output = kl.TimeDistributed(kl.Convolution1D(filters=128, kernel_size=4, activation='relu'))(emb_output) File "/home/user/anaconda3/envs/thesisenv/lib/python3

In AlexNet ,the image data is 3*224*224 . The first convolutional layer filters the image with 96 kernels of size 11*11*3 with a stride of 4 piexels. I have doubt with the first layer's output neurons count. In my opinion,the input is 224*224*3=150528 ,then the output should be 55*55*96=290400 But in the paper,they described the output is 253440 I don't know how to calculate the number of this layer's neurons. Is there someone could help me?Thank you! It seems like the input size is 227x227, without padding. I also think that what they mention in the paper is a mistake. Have look at this link.

Here is what I did. I got the code for dog/cat image classification and I compiled and ran and got 80% accuracy. I added one more class (aeroplane) folder to the train and validation folder. Made changes in the following codes model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='categorical') validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_width, img_height), batch_size

I am working on an image pixel classification problem using convolution neural nets. The size of my training images is 128x128x3 and the size of the label mask is 128x128 I do training in Keras as follows: Xtrain, Xvalid, ytrain, yvalid = train_test_split(images, masks,test_size=0.3, random_state=567) model.fit(Xtrain, ytrain, batch_size=32, epochs=20, verbose=1, shuffle=True, validation_data=(Xvalid, yvalid)) However, I want to apply a Random 2D rotation to Xtrain and ytrain which is also of size 128x128x3 and 128x128 respectively. More specifically, I want to apply this rotation for every