Category: Misc

I have a dataset that i downloaded from kaggle and it s in 4 folders trainA,trainB,testA,testB And i would like to put these image folders into a tf.data.dataset called DATA for example and i would like to call DATA[‘trainA’] for example to get the first folder thank you

I have a Segmentation network model trained for 2 classes and am able to see accurate results. But when using grad-cam for the heatmap, I am able to see good results for the last convolution layer for both the classes but have issues when trying to generate a heatmap for the second last convolution layer for one of the classes (the other class’s heatmap is working fine).

**Last 5 layers** convolution_layer(filters:8, kernel:3*3) convolution_transpose_layer(filters:2, kernel:2*2) convolution_layer(filters:2, kernel:3*3) convolution_layer(filters:10, kernel:1*1) activation_layer(softmax) 

The heatmap is empty because of all negative pooled gradients(due to mean from all the -ve gradients wrt Conv layer), resulting in negative values in pooled_grads*convolution_output on which relu is applied, giving all zeros.

What does it mean for GradCAM to be all negative?

Why is it that all channels in the convolution lead to a “negative” contribution to the true output class?

https://arxiv.org/pdf/2002.11434.pdf following this paper for heatmap for segmentation models.

Hi, I have been trying to implement a custom masked accuracy metric which does not consider the pad tokens (similar to the masked loss as shown here https://www.tensorflow.org/text/tutorials/nmt_with_attention). I have been trying to create a new subclass using the tf.keras. metrics.Metric, but I am very confused. Any leads would be really appreciated.

When I printed out the prediction, and the actual value side by side

[0.9785253] 6 [0.97852457] 5 [0.9785253] 6 [0.9785253] 5 [0.97848856] 6 [0.9785253] 7 [0.9785253] 5 [0.9785253] 7 

thats what it looked like, the value in bracket is the prediction, the other the actual one!

​

Have no clue why this is doing this, can you help please!!

I’ve tried to implement Yolov3 network by tf.keras, making it layer-by-layer. Then, I get outputs of layers 82, 94, 106, and pass them (and also – three training inputs with ground truth bounding boxes for every network stride) into Lambda layer to evaluate loss of net. However, when I try to train the network, I receive the warning: “WARNING:tensorflow:Gradients do not exist for variables [‘Layer_Conv_81/kernel:0’, ‘Layer_Conv_91/kernel:0’, ‘Layer_Batch_81/gamma:0’, ‘Layer_Batch_81/beta:0’, ‘Layer_Batch_91/gamma:0’, ‘Layer_Batch_91/beta:0’, ‘Output_1/kernel:0’, ‘Output_2/kernel:0’] when minimizing the loss. If you’re using `model.compile()`, did you forget to provide a `loss`argument?”

I’ve checked the sequence of layers – there are no unconnected ones, I have the loss function. What else could go wrong?

Brief version of code here:

def MakeYoloMainStructure(): inputImage = Input(shape=(IMAGE_SIDES[0], IMAGE_SIDES[1], 3), name='Main_Input') # Start placing layers layer1_1 = Conv2D(32, (3,3), strides=(1,1), use_bias=False, padding='same', name='Layer_Conv_1')(inputImage) layer1_2 = BatchNormalization(epsilon=eps, name='Layer_Batch_1')(layer1_1) layer1_3 = LeakyReLU(alpha=alp, name='Layer_Leaky_1')(layer1_2) # Start placing adding layers # Layer 1 - 64/1 layer2_1 = ZeroPadding2D(((1,0),(1,0)), name='Layer_ZeroPad_2')(layer1_3) layer2_2 = Conv2D(64, (3,3), strides=(2,2), use_bias=False, padding='valid', name='Layer_Conv_2')(layer2_1) layer2_3 = BatchNormalization(epsilon=eps, name='Layer_Batch_2')(layer2_2) layer2_4 = LeakyReLU(alpha=alp, name='Layer_Leaky_2')(layer2_3) ... layer80_2 = BatchNormalization(epsilon=eps, name='Layer_Batch_80')(layer80_1) layer80_3 = LeakyReLU(alpha=alp, name='Layer_Leaky_80')(layer80_2) layer81_1 = Conv2D(1024, (3,3), strides=(1,1), use_bias=False, padding='same', name='Layer_Conv_81')(layer80_3) # From this layer we make fork for first output (!) layer81_2 = BatchNormalization(epsilon=eps, name='Layer_Batch_81')(layer81_1) layer81_3 = LeakyReLU(alpha=alp, name='Layer_Leaky_81')(layer81_2) layer82_1 = Conv2D(3*6, (1,1), strides=(1,1), use_bias=False, padding='same', name='Output_1')(layer81_3) # FIRST output layer (!) layer84_1 = layer80_3 layer85_1 = Conv2D(256, (1,1), strides=(1,1), use_bias=False, padding='same', name='Layer_Conv_83')(layer84_1) ..... layer106_1 = Conv2D(3*6, (1,1), strides=(1,1), use_bias=False, padding='same', name='Output_3')(layer105_3) # THIRD output layer (!) # Net structure is completed yoloBoneModel = Model(inputImage, [layer82_1, layer94_1, layer106_1]) return yoloBoneModel def MakeYoloTrainStructure(yoloBoneModel): gridInput_all = [Input(shape=(GRID_SIDES[1], GRID_SIDES[1], 3, 6), name='Grid_Input_1'), Input(shape=(GRID_SIDES[2], GRID_SIDES[2], 3, 6), name='Grid_Input_2'), Input(shape=(GRID_SIDES[3], GRID_SIDES[3], 3, 6), name='Grid_Input_3')] layer_loss = Lambda(GetLoss, output_shape=(1,), name='GetLoss', arguments={'threshold': thresh})([*yoloBoneModel.output, *gridInput_all]) yoloTrainModel = Model([yoloBoneModel.input, *gridInput_all], layer_loss) return yoloTrainModel def GetLoss(args, threshold=0.5): modelOutputs = args[:3] checkInputs = args[3:] # ...... # Numerous manipulations to get loss of objects detection # ...... return loss def GetDataGenerator(batches): # Here I get image and ground truth Bounding Boxes data yield [imageData, *trueBoxes], np.zeros(batches) def main(): boneModel = MakeYoloMainStructure() trainModel = MakeYoloTrainStructure(boneModel) trainModel.compile(optimizer=Adam(lr=1e-3), loss={'GetLoss': lambda gridInput_all, y_pred: y_pred}, run_eagerly=True) batchSize = 32 trainModel.fit(GetDataGenerator(batchSize), steps_per_epoch=2000//batchSize, epochs=50, initial_epoch=0) 

Sorry if this was asked recently. I used google to search for this and pretty much only got old information. I know recently rdna 2 is supposed to work with ROCm but no one benchmarks the results.

I am looking to buy a new laptop soon and was hoping to get something in the “ultra portable” category with good battery life. Something with a 6800U or 6900HS. I had not been planning on getting one with a discrete gpu but the new Asus Zephyrus 14 has a 6800S, is fairly small considering, gets 8 to 10 hours on battery. Might get a lenovo Z13 if the price is right when it releases.

I mostly just ssh into my desktop at home so I am not buying the laptop specifically for deep learning but I cant find anything I quite want in the laptop space with a 3060/3070. Obviously I would only do small test type work if I ever have to run it on the laptop and cpu training is possible, but would a 6800S gpu or maybe even the 680m onboard graphics train faster than the cpu?

Hi,

In deep rl algorithm like PPO, a continuous stochastic policy is represented by Normal Distribution. For this the recommended way of creating a Normal Distribution is to get the mean by passing the state through NN and then using a state independent layer to predict log_std. This layer which predicts log_std should be trainable using backprop just like biases. So how to create this layer in tensorflow 2.

How can I install TF 1.x with CUDA 11.x with GPU support (for RTX 3xxx series)? All tutorials are outdated today. Wanna run a Mask R-CNN. Even the one with TF 2.x support, doesnt work with CUDA 11.x, rather than with CUDA 10.x