I want to use ResNet model architecture and want to change last few layers; how can I only use model architecture from model zoo in Tensorflow?
To use a ResNet model, you can choose a select few from tensorflow.keras.applications including ResNet50, ResNet101, and ResNet152. Then, you will need to change a few of the default arguments if you want to do transfer learning. For your question, you will need to set the weights parameter equal to None. Otherwise, 'imagenet' weights are provided. Also, you need to set include_top to be False since the number of classes for your problem will likely be different from ImageNet. Finally, you will need to provide the shape of your data in input_shape. This would look something like this.
base = tf.keras.applications.ResNet50(include_top=False, weights=None, input_shape=shape)
To get a summary of the model, you can do
base.summary()
To add your own head, you can use the Functional API. You will need to add an Input layer and your own Dense layer which will correspond to your task. This could be
input = tf.keras.layers.Input(shape=shape)
base = base(input)
out = tf.keras.layers.Dense(num_classes, activation='softmax')(base)
Finally, to construct a model, you can do
model = tf.keras.models.Model(input, out)
The Model constructor takes 2 arguments. The first being the inputs to your model, and the second being the outputs. Note that calling model.summary() will show the ResNet base as a separate layer. To view all layers of the ResNet base, you can do model.layers[1].summary(), or you can modify the code on how you built your model. The second way would be
out = tf.keras.layers.Dense(num_classes, activation='softmax')(base.output)
model = tf.keras.models.Model(base.input, out)
Now you can view all layers with just model.summary().
Related
If the pretrained model such as Resnet101 were trained on ImageNet dataset, then I change some layers inside it. Can I still be able to use the pretrained model on different ABC dataset?
Lets say This is ResNet34 Model,
It is pretrained on ImageNet and saved as ResNet.pt file.
If I changed some layers inside it, lets say I made it more deeper by introducing some layers in conv4_x (check image)
model = Resnet34() #I have changes some layers inside this ResNet34()
optimizer = optim.Adam(model.parameters(), lr=0.00005)
model.load_state_dict(torch.load('Resnet.pt')['state_dict']) #This is pretrained model of ResNet before some changes
optimizer.load_state_dict(torch.load('Resnet.pt')['optimizer'])
Can I do this? or there are anyother method?
You can do anything you like - the question is: would it be better than training from scratch?
Here are a few issues you might encounter:
1. A mismatch between weights saved in ResNet.pt (the trained weights of the original ResNet18) and the state_dict of your modified model.
You would probably need to manually make sure that the old weights are correctly assigned to the original layers and only the new layer is not initialized.
2. Initializing the weights of the new layer.
Since you are training a resNet - you can take advantage of the residual connections and init the weights of the new layer such that it would initially make no contribution to the predicted value and only pass the input directly to the output via the residual link.
Really don't have much idea of what I'm doing, followed this tutorial to process deepdream images https://www.youtube.com/watch?v=Wkh72OKmcKI
Trying to change the base model data set to any from here, https://keras.io/api/applications/#models-for-image-classification-with-weights-trained-on-imagenet particularly InceptionResNetV2 currently. InceptionV3 uses "mixed0" up to "mixed10" whereas, the former data set uses a different naming system apparently.
Would have to change this section
# Maximize the activations of these layers
names = ['mixed3', 'mixed5']
layers = [base_model.get_layer(name).output for name in names]
# Create the feature extraction model
dream_model = tf.keras.Model(inputs=base_model.input, outputs=layers)
I'm getting an error "no such layer: mixed3"
So yea, just trying to figure out how to get the names for the layers in this data set as well as others
You can simply enter the following code to find out the model architecture(including layer names).
#Model denotes the Inception model
model.summary()
Or to visualize complex relationships,
tf.keras.utils.plot_model(model, to_file='model.png')
I want to train a model in a sequential manner. That is I want to train the model initially with a simple architecture and once it is trained, I want to add a couple of layers and continue training. Is it possible to do this in Keras? If so, how?
I tried to modify the model architecture. But until I compile, the changes are not effective. Once I compile, all the weights are re-initialized and I lose all the trained information.
All the questions in web and SO I found are either about loading a pre-trained model and continuing training or modifying the architecture of pre-trained model and then only test it. I didn't find anything related to my question. Any pointers are also highly appreciated.
PS: I'm using Keras in tensorflow 2.0 package.
Without knowing the details of your model, the following snippet might help:
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Input
# Train your initial model
def get_initial_model():
...
return model
model = get_initial_model()
model.fit(...)
model.save_weights('initial_model_weights.h5')
# Use Model API to create another model, built on your initial model
initial_model = get_initial_model()
initial_model.load_weights('initial_model_weights.h5')
nn_input = Input(...)
x = initial_model(nn_input)
x = Dense(...)(x) # This is the additional layer, connected to your initial model
nn_output = Dense(...)(x)
# Combine your model
full_model = Model(inputs=nn_input, outputs=nn_output)
# Compile and train as usual
full_model.compile(...)
full_model.fit(...)
Basically, you train your initial model, save it. And reload it again, and wrap it together with your additional layers using the Model API. If you are not familiar with Model API, you can check out the Keras documentation here (afaik the API remains the same for Tensorflow.Keras 2.0).
Note that you need to check if your initial model's final layer's output shape is compatible with the additional layers (e.g. you might want to remove the final Dense layer from your initial model if you are just doing feature extraction).
I have trained a fully convolutional neural network with Keras. I have used the Functional API and have defined the input layer as Input(shape=(128,128,3)), corresponding to the size of the images in my training set.
However, I want to use the trained model on images of variable sizes (which should be ok because the network is fully convolutional). To do this, I need to change my input layer to Input(shape=(None,None,3)). The obvious way to solve the problem would have been to train my model directly with an input shape of (None,None,3) but I use a custom loss function where I need to specify the size of my training images.
I have tried to define a new input layer and assign it to my model like this :
from keras.engine import InputLayer
input_layer = InputLayer(input_shape=(None, None, 3), name="input")
model.layers[0] = input_layer
This actually changes the size of the input layers accordingly but the following layers still expect (128,128,filters) inputs.
Is there a way to change all of the inputs values at once ?
Create a new model, exactly the same, except for new input shape; and tranfer weights:
newModel.set_weights(oldModel.get_weights())
If anything goes wrong, then it might not be fully convolutional (ex: contains a Flatten layer).
Here is what I wanna do:
I want to use some transfer learning techniques to deal with sequence problems:
First use dataset_1 to train a lstm model,
Second insert another lstm layer before the output layer,
and then use dataset_2 to only train the newly adding layer, and the variables of other layers are imported from the first training stage and remain unchanged
here's the problem, the existing methods all require the variable name of weight/bias when restoring the pre-trained model. And I want to use the fuction tf.contrib.rnn.MultiRNNCell(*) in my code.However, the function is a black box and unable to obtain concrete variable names. How can I realize the idea?