conv-neural-network

问题 So I am currently trying to build a race recognition program using a convolution neural network. I'm inputting 200px by 200px versions of the UTKFaceRegonition dataset (put my dataset on a google drive if you want to take a look). Im using 8 different classes (4 races * 2 genders) using keras and tensorflow, each having about 700 images but I have done it with 1000. The problem is when I run the network it gets at best 13.5% accuracy and about 11-12.5% validation accuracy, with a loss around

问题 I am working on a problem for predicting a score of how fat cows are, based on images of cows. I applied a CNN to estimate the value which is between 0-5 ( the dataset i have, contains only values between 2.25 and 4 ) I am using 4 CNN layers and 3 Hidden layers. I actualy have 2 problems : 1/ I got 0.05 training error, but after 3-5 epochs the validation error remains at about 0.33. 2/ The value predicted by my NN are between 2.9 and 3.3 which is too narrow compared with the dataset range. Is

问题 I am fine-tuning a MobileNet with 14 new classes. When I add new layers by: x=mobile.layers[-6].output x=Flatten(x) predictions = Dense(14, activation='softmax')(x) model = Model(inputs=mobile.input, outputs=predictions) I get the error: 'Tensor' object has no attribute 'lower' Also using: model.compile(Adam(lr=.0001), loss='categorical_crossentropy', metrics=['accuracy']) model.fit_generator(train_batches, steps_per_epoch=18, validation_data=valid_batches, validation_steps=3, epochs=60,

问题 I try to create a CNN model, but always get this error message. Error: TypeError Traceback (most recent call last) in () ----> 1 model = simple_conv_model() 5 frames /usr/local/lib/python3.6/dist-packages/keras/engine/network.py in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index) 1345 1346 # Propagate to all previous tensors connected to this node. -> 1347 for i in range(len(node.inbound_layers)): 1348 x = node.input_tensors[i] 1349 layer = node.inbound

问题 I am doing a image classification project using CNN in keras. I have a dataset of about 900 photos of about 70 people .Each person has multiple photos of his different age. My goal is to predict the correct ID of the person if any one of his photo is in the input. Here is the glimpse of the data. My questions are: What should be my target column ?Is Target 'AGE' or 'ID'? 2-Do I need to do hot-encoding of the target column? For example if I used ID as my target,then do I have to do one-hot

问题 I am doing a image classification project using CNN in keras. I have a dataset of about 900 photos of about 70 people .Each person has multiple photos of his different age. My goal is to predict the correct ID of the person if any one of his photo is in the input. Here is the glimpse of the data. My questions are: What should be my target column ?Is Target 'AGE' or 'ID'? 2-Do I need to do hot-encoding of the target column? For example if I used ID as my target,then do I have to do one-hot

问题 I want to perform instance segmentation using SSD model. For this, I have used an FCN model to perform instance segmentation and now can I combine this with SSD model to get something like Mask SSD ? It should look something like this or something like this 来源： https://stackoverflow.com/questions/61734815/how-to-combine-a-fully-convolutional-neural-network-with-a-ssd-mobilenet-model

问题 I am training a CNN model. I am facing issue while doing the training iteration for my model. The code is as below: class Net(nn.Module): def __init__(self): super(Net, self).__init__() #convo layers self.conv1 = nn.Conv2d(3,32,3) self.conv2 = nn.Conv2d(32,64,3) self.conv3 = nn.Conv2d(64,128,3) self.conv4 = nn.Conv2d(128,256,3) self.conv5 = nn.Conv2d(256,512,3) #pooling layer self.pool = nn.MaxPool2d(2,2) #linear layers self.fc1 = nn.Linear(512*5*5,2048) self.fc2 = nn.Linear(2048,1024) self

问题 I'm trying to build a CNN + RNN using a pre-trained model from tensorflow-hub: base_model = hub.KerasLayer('https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4', input_shape=(244, 244, 3) base_model.trainable = False model = Sequential() model.add(TimeDistributed(base_model, input_shape=(15, 244, 244, 3))) model.add(LSTM(512)) model.add(Dense(256, activation='relu')) model.add(Dense(3, activation='softmax')) adam = Adam(learning_rate=learning_rate) model.compile(loss='categorical

问题 I'm trying to build a CNN + RNN using a pre-trained model from tensorflow-hub: base_model = hub.KerasLayer('https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4', input_shape=(244, 244, 3) base_model.trainable = False model = Sequential() model.add(TimeDistributed(base_model, input_shape=(15, 244, 244, 3))) model.add(LSTM(512)) model.add(Dense(256, activation='relu')) model.add(Dense(3, activation='softmax')) adam = Adam(learning_rate=learning_rate) model.compile(loss='categorical