you can change the factor parameter to change that. By default it is set to 3, but you can increase this number to get more than 2 epoch per trial see : docs The Hyperband tuning algorithm uses adaptive resource allocation and early-stopping to quickly converge on a high-performing model. This is done using a … Read more
Use keras backend to fit the input tensor to 2D transpose convolution. Do not always use transpose operation for it will consume a lot of time. import keras.backend as K from keras.layers import Conv2DTranspose, Lambda def Conv1DTranspose(input_tensor, filters, kernel_size, strides=2, padding=’same’): “”” input_tensor: tensor, with the shape (batch_size, time_steps, dims) filters: int, output dimension, i.e. … Read more
The easy answer is don’t use a sequential model for this, use the functional API instead, implementing skip connections (also called residual connections) are then very easy, as shown in this example from the functional API guide: from keras.layers import merge, Convolution2D, Input # input tensor for a 3-channel 256×256 image x = Input(shape=(3, 256, … Read more
You don’t have to downgrade Keras 2.2.2. In Keras 2.2.2 there is no _obtain_input_shape method in the keras.applications.imagenet_utils module. You can find it under keras-applications with the modul name keras_applications (underscore). So you don’t have to downgrade your Keras to 2.2.0 just change: from keras.applications.imagenet_utils import _obtain_input_shape to from keras_applications.imagenet_utils import _obtain_input_shape
These 2048 parameters are in fact [gamma weights, beta weights, moving_mean(non-trainable), moving_variance(non-trainable)], each having 512 elements (the size of the input layer).
When you use fit_generator, the number of samples processed for each epoch is batch_size * steps_per_epochs. From the Keras documentation for fit_generator: https://keras.io/models/sequential/ steps_per_epoch: Total number of steps (batches of samples) to yield from generator before declaring one epoch finished and starting the next epoch. It should typically be equal to the number of unique … Read more
This would not be a standard approach to deal with unbalanced data. Nor do I think it would be really justified – you would be significantly changing the distributions of your classes, where the smaller class is now much less variable. The larger class would have rich variation, the smaller would be many similar images … Read more
If there’s a mask in your model, it’ll be propagated layer-by-layer and eventually applied to the loss. So if you’re padding and masking the sequences in a correct way, the loss on the padding placeholders would be ignored. Some Details: It’s a bit involved to explain the whole process, so I’ll just break it down … Read more
The key is to first do .get_layer on the Model object, then do another .get_layer on that specifying the specific vgg16 layer, THEN do .output: layer_output = model.get_layer(‘vgg16’).get_layer(‘block3_conv1’).output
As you are trying to use function decorator in TF 2.0, please enable run function eagerly by using below line after importing TensorFlow: tf.config.experimental_run_functions_eagerly(True) Since the above is deprecated(no longer experimental?), please use the following instead: tf.config.run_functions_eagerly(True) If you want to know more do refer to this link.