Gradients can be fetched w.r.t. weights or outputs – we’ll be needing latter. Further, for best results, an architecture-specific treatment is desired. Below code & explanations cover every possible case of a Keras/TF RNN, and should be easily expandable to any future API changes. Completeness: code shown is a simplified version – the full version … Read more
First you must learn that in Keras when you apply a layer on an input, a new node is created inside this layer which connects the input and output tensors. Each layer may have multiple nodes connecting different input tensors to their corresponding output tensors. To build a model, these nodes are traversed and a … Read more
Except providing an answer to your question I will make the code more TF2.0-like. If you have any questions/need clarification, please post a comment down below. 1. Loading data I would advise to use Tensorflow Datasets library. There is absolutely no need to load data in numpy and transform it to tf.data.Dataset if one can … Read more
Looks like I found the solution. As I have suggested the problem is with BatchNormalization layers. They make tree things subtract mean and normalize by std collect statistics on mean and std using running average train two additional parameters (two per node). When one sets trainable to False, these two parameters freeze and layer also … Read more
you can still usecontrol_dependencies def mean_iou(y_true, y_pred): score, up_opt = tf.metrics.mean_iou(y_true, y_pred, NUM_CLASSES) K.get_session().run(tf.local_variables_initializer()) with tf.control_dependencies([up_opt]): score = tf.identity(score) return score
Quite simple, its just the second element in the array returned by get_weights() (For Dense layers): B_Input_Hidden = model.layers[0].get_weights()[1] B_Output_Hidden = model.layers[1].get_weights()[1]
I’ve found some workaround to plot with the model sub-classing API. For the obvious reason Sub-Classing API doesn’t support Sequential or Functional API like model.summary() and nice visualization using plot_model. Here, I will demonstrate both. class my_model(keras.Model): def __init__(self, dim): super(my_model, self).__init__() self.Base = keras.keras.applications.VGG16( input_shape=(dim), include_top = False, weights=”imagenet” ) self.GAP = L.GlobalAveragePooling2D() self.BAT … Read more
The difference between tf.keras and keras is the Tensorflow specific enhancement to the framework. keras is an API specification that describes how a Deep Learning framework should implement certain part, related to the model definition and training. Is framework agnostic and supports different backends (Theano, Tensorflow, …) tf.keras is the Tensorflow specific implementation of the … Read more
Reading from here, which is the source code of KerasClassifier, you can pass it the arguments of fit and they should be used. I don’t have your dataset so I cannot test it, but you can tell me if this works and if not I will try and adapt the solution. Change this line : … Read more
OOM stands for “out of memory”. Your GPU is running out of memory, so it can’t allocate memory for this tensor. There are a few things you can do: Decrease the number of filters in your Dense, Conv2D layers Use a smaller batch_size (or increase steps_per_epoch and validation_steps) Use grayscale images (you can use tf.image.rgb_to_grayscale) … Read more