I've got an imported model with type of 'keras.engine.training.Model' and I want a Sequential model.
I tried this:
model = ..imported model..
seq_model = Sequential()
for layer in model.layers:
seq_model.add(layer)
But it said that "ValueError: A merge layer should be called on a list of inputs."
If you're trying to insert ResNet, Please check functional API to use it as an output layer.
However, e.g: if you're using VGG16 please check this.
Related
To solve the issue that I've posted here : Adjust the output of a CNN as an input for TimeDistributed tensorflow layer which is about input data format of the Time distributed tensorflow layer, I think about another idea: instead of passing two inputs to a CNN model, what if , before designing the CNN model, I merge the two inputs in one input using pandas or numpy, and then pass it to the CNN model and then AFTER the INPUT LAYER and BEFORE the CONVOLUTION LAYER, I add a customized layer that separate feature that I concatenate them !! Is this possible ? the following picture explain more what I am talking about:
Thank you #Marco for the help. Exactly like Marco says, I separate the input using index slicing and was done using a Lambda layer. This is the code:
input_layer1=tf.keras.Input(shape=(input_shape))
separate_features1 = tf.keras.layers.Lambda(lambda x : tf.transpose(x,[0,1,2,3])[:,:-1,:,:])(input_layer1)
separate_features2 = tf.keras.layers.Lambda(lambda x : tf.transpose(x,[0,1,2,3])[:,-1:,:,:])(input_layer1)
This is the model architecture:
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 have a few Tensorflow models saved as .h5 files.
Due to poor record-keeping and documentation on my part, I can't recall the exact architecture each has. So, I was wondering if there was a way, from the h5 files saved for each model, to inspect the models and determine the architecture.
For example, is there a way to find out the number of layers, the activation functions, input/ouput, size, etc.
Any help is appreciated.
Thanks,
Sam
As suggested by Edwin in comments you can load the model and see the summary for layer details.
You can use the below code to get both the information about activation function and model architecture.
from tensorflow.keras.models import load_model
model = load_model('saved_model.h5')
for layer in model:
try:
print(layer.activation)
#for some layers there will not be any activation fucntion.
except:
pass
#To get the name of layers in the model.
layer_names=[layer.name for layer in model.layers]
#for model's summary and details.
model.summary()
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().
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).