I am using one layer to extract features from image. The old layer is
self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
New layer is
resConv = models.resnet50(pretrained=True)
resConv.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.conv = resConv.conv1
Any performance difference? or both layers are same.
Almost, there are a few differences however. According to the paper https://arxiv.org/abs/1512.03385, resnet50's conv1 layer (as you would find in torchvision.models.resnet50()) has
conv1 = {Conv2d} Conv2d(3, 64, kernel_size=(7,7), stride=(2,2), padding=(3,3), bias=False).
So the differences area) the kernel_size is 7x7 not 3x3b) padding of 3x3 not 1x1 andc) the weights from resnet50.conv1 from the pretrained model will be conditioned on the training and not initialized as random normal as the will be for nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
The main difference here is, in self.Conv, weights are default (for example zero) and need to train, but in resConv.Conv1 which uses a pre-trained model, weights are tuned because it is trained with large datasets before.
performance result depends on your task but in general, it helps to get a better result (better local optimal) or need fewer epoch to train.
Related
I'm trying to build a Siamese Network for https://www.kaggle.com/moltean/fruits dataset. I've picked 10 Images per class from this dataset. There are a total of 131 classes in this dataset. I'm using the below model to train my network. However, it is failing to converge. I saw a strange behaviour, after 3000 epochs my results are 0.5000003 irrespective of the input pair I give and my loss stops at 0.61. The specifications of the network are as specified in the paper. I tried changing the following things,
Changing Denes layer activation to ReLU
Importing 'ImageNet' weights of ResNet50
Tried increasing and decreasing learning rate.
I also checked the batch inputs to see if the correct input pair (x) is paired with the correct y value. However, I think I'm doing something basically wrong. Glad if you could help me. Thank you :)
The notebook is hosted in Kaggle https://www.kaggle.com/krishnaprasad96/siamese-network.
If you have some doubts on how certain parts of the code works refer https://medium.com/#krishnaprasad_54871/siamese-networks-line-by-line-explanation-for-beginners-55b8be1d2fc6
#Building a sequential model
input_shape=(100, 100, 3)
left_input = Input(input_shape)
right_input = Input(input_shape)
W_init = keras.initializers.RandomNormal(mean = 0.0, stddev = 1e-2)
b_init = keras.initializers.RandomNormal(mean = 0.5, stddev = 1e-2)
model = keras.models.Sequential([
keras.layers.Conv2D(64, (10,10), activation='relu', input_shape=input_shape, kernel_initializer=W_init, kernel_regularizer=l2(2e-4)),
keras.layers.MaxPooling2D(),
keras.layers.Conv2D(128, (7,7), activation='relu', kernel_initializer=W_init, bias_initializer=b_init, kernel_regularizer=l2(2e-4)),
keras.layers.MaxPooling2D(),
keras.layers.Conv2D(128, (4,4), activation='relu', kernel_initializer=W_init, bias_initializer=b_init, kernel_regularizer=l2(2e-4)),
keras.layers.MaxPooling2D(),
keras.layers.Conv2D(256, (4,4), activation='relu', kernel_initializer=W_init, bias_initializer=b_init, kernel_regularizer=l2(2e-4)),
keras.layers.MaxPooling2D(),
keras.layers.Flatten(),
keras.layers.Dense(4096, activation='sigmoid', kernel_initializer=W_init, bias_initializer=b_init, kernel_regularizer=l2(1e-3))
])
encoded_l = model(left_input)
encoded_r = model(right_input)
subtracted = keras.layers.Subtract()([encoded_l, encoded_r])
prediction = Dense(1, activation='sigmoid', bias_initializer=b_init)(subtracted)
siamese_net = Model(input=[left_input, right_input], output=prediction)
optimizer= Adam(learning_rate=0.0006)
siamese_net.compile(loss='binary_crossentropy', optimizer=optimizer)
plot_model(siamese_net, show_shapes=True, show_layer_names=True)
I have seen the notebook on kaggle. Thanks for all the information. But it seems that training and validation spilt is wrong. As this model trains on initial 91 classes only. What about remaining 40 classes. Train and validation spilt should be from the same class. Suppose I have 10 images in a class. I can use 8 image for train and 2 images for validation. Train and validation spilt should be on images not on classes. Also I couldn't see the testing script. It would be a great help if you can provide that also.
I am working with a Boston housing price. I have my X and Y with a shape of (506, 13). Then, i define my model
def basic_model_1():
t_model = Sequential()
t_model.add(Dense(13, activation="tanh", input_dim = 13))
t_model.add(Dense(10, activation="tanh"))
t_model.add(Dropout(0.2))
t_model.add(Dense(6, activation="tanh"))
t_model.add(Dense(3, activation="tanh"))
t_model.add(Dense(1))
print(t_model.summary())
t_model.compile(loss='mean_squared_error',
optimizer='adam',
metrics=['accuracy'])
t_model.fit(X,Y, nb_epoch = 200 , batch_size= 10, validation_split= 0.20)
return(t_model)
When i run this model, i get pretty bad performance of val_acc 0.0098. I changed activation function to sigmoid or relu. Performance increases slightly. What do i need to increase model performance?
In my opinion you could:
1) Add more neurons at every layer (use a multiple of 2 for better performance, try 64, 128, 256).
2) Add more dropout layers, one after every Dense layer.
3) Add much more data.
There is nothing wrong with your model architecture. Only thing I would suggest is to use kernel_initializer='normal', activation='relu' in all Dense layers (specially in output layer) since it's a regression model.
A Triplet network (inspired by "Siamese network") is comprised of 3 instances of the same feed-forward network (with shared parameters). When fed with 3 samples, the network outputs 2 intermediate values - the L2 (Euclidean) distances between the embedded representation of two of its inputs from
the representation of the third.
I'm using pairs of three images for feeding the network (x = anchor image, a standard image, x+ = positive image, an image containing the same object as x - actually, x+ is same class as x, and x- = negative image, an image with different class than x.
I'm using the triplet loss cost function described here.
How do I determine the network's accuracy?
I am assuming that your are doing work for image retrieval or similar tasks.
You should first generate some triplet, either randomly or using some hard (semi-hard) negative mining method. Then you split your triplet into train and validation set.
If you do it this way, then you can define your validation accuracy as proportion of the number of triplet in which feature distance between anchor and positive is less than that between anchor and negative in your validation triplet. You can see an example here which is written in PyTorch.
As another way, you can directly measure in term of your final testing metric. For example, for image retrieval, typically, we measure the performance of model on test set using mean average precision. If you use this metric, you should first define some queries on your validation set and their corresponding ground truth image.
Either of the above two metric is fine. Choose whatever you think fit your case.
So I am performing a similar task of using Triplet loss for classification. Here is how I used the novel loss method with a classifier.
First, train your model using the standard triplet loss function for N epochs. Once you are sure that the model ( we shall refer to this as the embedding generator) is trained, save the weights as we shall be using these weights ahead.
Let's say that your embedding generator is defined as:
class EmbeddingNetwork(nn.Module):
def __init__(self):
super(EmbeddingNetwork, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(1, 64, (7,7), stride=(2,2), padding=(3,3)),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.001),
nn.MaxPool2d((3, 3), 2, padding=(1,1))
)
self.conv2 = nn.Sequential(
nn.Conv2d(64,64,(1,1), stride=(1,1)),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.001),
nn.Conv2d(64,192, (3,3), stride=(1,1), padding=(1,1)),
nn.BatchNorm2d(192),
nn.LeakyReLU(0.001),
nn.MaxPool2d((3,3),2, padding=(1,1))
)
self.fullyConnected = nn.Sequential(
nn.Linear(7*7*256,32*128),
nn.BatchNorm1d(32*128),
nn.LeakyReLU(0.001),
nn.Linear(32*128,128)
)
def forward(self,x):
x = self.conv1(x)
x = self.conv2(x)
x = self.fullyConnected(x)
return torch.nn.functional.normalize(x, p=2, dim=-1)
Now we shall using this embedding generator to create another classifier, fit the weights we saved before to this part of the network and then freeze this part so our classifier trainer does not interfere with the triplet model. This can be done as:
class classifierNet(nn.Module):
def __init__(self, EmbeddingNet):
super(classifierNet, self).__init__()
self.embeddingLayer = EmbeddingNet
self.classifierLayer = nn.Linear(128,62)
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x = self.dropout(self.embeddingLayer(x))
x = self.classifierLayer(x)
return F.log_softmax(x, dim=1)
Now we shall load the weights we saved before and freeze them using:
embeddingNetwork = EmbeddingNetwork().to(device)
embeddingNetwork.load_state_dict(torch.load('embeddingNetwork.pt'))
classifierNetwork = classifierNet(embeddingNetwork)
Now train this classifier network using the standard classification losses like BinaryCrossEntropy or CrossEntropy.
I use Keras and I try to concatenate two different layers into a vector (first values of the vector would be values of the first layer, and the other part would be the values of the second layer).
One of these layers is a Dense layer and the other layer is a Embedding layer.
I know how to merge two embedding layers or two dense layers but I don't know how to merge a embedding layer and a dense layer (dimensional problem).
A simple example would be like this:
L_branch = Sequential()
L_branch.add(Dense(10, input_shape = (4,) , activation = 'relu'))
L_branch.add(BatchNormalization())
R_branch = Sequential()
R_branch.add(Embedding(1000, 64, input_length=5))
final_branch.add(Merge([L_branch, R_branch], mode = 'concat'))
But this will not work because you can't merge layers with different dimensionalities.
PS : Sorry, english is not my native languague and I hope you will understand my problem.
Best regards.
Use Flatten layer.
L_branch = Sequential()
L_branch.add(Dense(10, input_shape = (4,) , activation = 'relu'))
L_branch.add(BatchNormalization())
R_branch = Sequential()
R_branch.add(Embedding(1000, 64, input_length=5))
R_branch.add(Flatten()) # <--
final_branch = Sequential() # <--
final_branch.add(Merge([L_branch, R_branch], mode = 'concat'))
I am trying to emulate something equivalent to a SeparableConvolution2D layer for the theano backend (it already exists for the TensorFlow backend). As the first step What I need to do is pass ONE channel from a tensor into the next layer. So say I have a 2D convolution layer called conv1 with 16 filters which produces an output with shape: (batch_size, 16, height, width) I need to select the subtensor with shape (: , 0, : , : ) and pass it to the next layer. Simple enough right?
This is my code:
from keras import backend as K
image_input = Input(batch_shape = (batch_size, 1, height, width ), name = 'image_input' )
conv1 = Convolution2D(16, 3, 3, name='conv1', activation = 'relu')(image_input)
conv2_input = K.reshape(conv1[:,0,:,:] , (batch_size, 1, height, width))
conv2 = Convolution2D(16, 3, 3, name='conv1', activation = 'relu')(conv2_input)
This throws:
Exception: You tried to call layer "conv1". This layer has no information about its expected input shape, and thus cannot be built. You can build it manually via: layer.build(batch_input_shape)
Why does the layer not have the required shape information? I'm using reshape from the theano backend. Is this the right way of passing individual channels to the next layer?
I asked this question on the keras-user group and I got an answer there:
https://groups.google.com/forum/#!topic/keras-users/bbQ5CbVXT1E
Quoting it:
You need to use a lambda layer, like: Lambda(x: x[:, 0:1, :, :], output_shape=lambda x: (x[0], 1, x[2], x[3]))
Note that such a manual implementation of a separable convolution would be horribly inefficient. The correct solution is to use the TensorFlow backend.