I have two models, their structure are exactly the same except
The data_format of Conv2D layers in first model is channels_first, while second model is channels_last
First model accepts NCHW tensor input while the second model accepts NHWC
What I want to do is :
Using get_weights to retrieve the weights from layer of first model, and then transform the weights and fill into the corresponding layer of second model by set_weights
The model only contains Conv2D and Dense layers, I assume Dense's weights also need be transformed since their inputs changed.
How should I do it?
P.S. I tried ONNX convert but failed, that's another question.
Related
I want to create a CNN model using the concatenation of hidden layers two pretrained models Resnet and VGG16
After you define model, checkout these pretrained models layers by model.summary(), then when you define layer, try to take output of that layer in this way; first get the model.get_layer('layer_name') and then take its output by layer.output, and now concatenate the outputs of the layers that you have defined before.
I am fairly new to TF, Keras and ML in general.
I am trying to implement a very simple MLP with an input shape of (batch_size,3,2) and an output shape of (batch_size,3), that is (if I got it right): for every 3x2 feature, there is a corresponding 3 value array label.
Here is how I create the model:
model = tf.keras.Sequential([
tf.keras.layers.Dense(50,tf.keras.activations.relu,input_shape=((3,2)),
tf.keras.layers.Dense(3)
])
and these are the X and y shapes:
X_train.shape,y_train.shape
TensorShape([64,3,2]),TensorShape([64,3])
On model.fit I am facing a weird error I cannot understand:
ValueError: Dimensions must be equal, but are 3 and 32 for ... with input shapes: [32,3,3] and [32,3]
I have no clue what's going on, I understand the batch size is 32, but where does that [32,3,3] comes from?
Moreover, if from the original 64, I lower the number (shapes) of X_train and y_train, say, to: (19,3,2) and (19,3), I get the following error instead:
InvalidArgumentError: required broadcastable shapes at loc(unknown)
What's even more weird for me is that if I specify a single unit for the output (last) layer, instead of 3 like this:
model = tf.keras.Sequential([
tf.keras.layers.Dense(50,tf.keras.activations.relu,input_shape=((3,2)),
tf.keras.layers.Dense(1)
])
model.fit works, but the predictions have shape (1,3,1) instead of my expected (3,)
I am very confused.
Whenever you have not any idea about the journey of data throughout your model, use model.summary() to see the details and what happens to the shape of data in each layer.
In this case, the input is a 2D array, and the output is a 1D array, and you just used dense layers. Dense layers can not handle 2d features in nature. For example for an image as input, you can not feed it directly to a dense layer. Instead you should use other layers such as Conv2D or Flatten your input (make it 1D) before feeding your data to the dense layer. Otherwise you will get the other dimension in the output.
Inference: If your input dimension and output dimension differs, somewhere in your model, the shape need to be changed. Most common ways to do so, is using a Flatten layer or GlobalAveragePooling and so on.
When you pass an input to a dense layer, the input should be flattened first. There are 2 ways to deal with this:
Way 1: Adding a flatten input as a first layer of your model:
model = Sequential()
model.add(Flatten(input_shape=(3,2)))
model.add(Dense(50, 'relu'))
model.add(Dense(3))
Way 2: Converting the 2D array to 1D before passing the inputs to your model:
X_train = tf.reshape(X_train, shape=([6]))
or
X_train = tf.reshape(X_train, shape=((6,)))
Then change the input shape of the first layer as:
model.add(Dense(50, 'relu', input_shape=(6,))
I was working on an image recognition problem. After training the model, I saved the architecture as well as weights. Now I want to use the model for extracting features from other images and perform SVM on that. For this, I want to remove the last two layers of my model and get the values calculated by the CNN and fully connected layers till then. How can I do that in Keras?
# a simple model
model = keras.models.Sequential([
keras.layers.Input((32,32,3)),
keras.layers.Conv2D(16, 3, activation='relu'),
keras.layers.Flatten(),
keras.layers.Dense(10, activation='softmax')
])
# after training
feature_only_model = keras.models.Model(model.inputs, model.layers[-2].output)
feature_only_model take a (32,32,3) for input and the output is the feature vector
If your model is subclassed - just change call() method.
If not:
if your model is complicated - wrap your model by subclassed model and change forward pass in call() method, or
if your model is simple - create model without the last layers, load weights to every layer separately
I am unsure if I need to add a Dense input layer before adding LSTM layers in my model. Forexample, with the following model:
# Model
model = Sequential()
model.add(LSTM(128, input_shape=(train_x.shape[1], train_x.shape[2])))
model.add(Dense(5, activation="linear"))
Will the LSTM layer be the input layer, and the Dense layer the output layer (meaning no hidden layers)? Or does Keras create an input layer meaning the LSTM layer will be a hidden layer?
You don't need too. It depends on what you want to accomplish.
Check here some cases.
In your case, yes the LSTm will be the first layer and the Dense layer will be the output layer.
The current configuration is okay for simple examples. Everything is based on what you want to get as results. The model and layers subject to change based on the target goal. So, if the model is complex, you can make mix model with different layers and shapes. See reference.
Mix model layering
I'm searching for a data leak in my model. I'm using tf.layers.dense before a masking operation and am concerned that the model could just learn to switch positions in the middle dimension of my input tensor.
When I have an input tensor x = tf.ones((2,3,4)) would tf.layers.dense(x,8) flatten x to a fully connected layer with 2*3*4=24 input neurons and 2*3*8=48 output neurons then reshape it again to [2,3,8], or would it create 2*3=6 fully connected layers with 4 input and 8 output neurons then concatenate them?
As for the Keras Dense layer, it has been already mentioned in another answer that its input is not flattened and instead, it is applied on the last axis of its input.
As for the TensorFlow Dense layer, it is actually inherited from Keras Dense layer and as a result, same as Keras Dense layer, it is applied on the last axis of its input.