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
I’ve added built-in support for keras in tqdm so you could use it instead (pip install “tqdm>=4.41.0”): from tqdm.keras import TqdmCallback … model.fit(…, verbose=0, callbacks=[TqdmCallback(verbose=2)]) This turns off keras‘ progress (verbose=0), and uses tqdm instead. For the callback, verbose=2 means separate progressbars for epochs and batches. 1 means clear batch bars when done. 0 means … Read more
*Edited to include relevant code to implement permutation importance. I answered a similar question at Feature Importance Chart in neural network using Keras in Python. It does implement what Teque5 mentioned above, namely shuffling the variable among your sample or permutation importance using the ELI5 package. from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor import eli5 from eli5.sklearn … 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
The Flatten() operator unrolls the values beginning at the last dimension (at least for Theano, which is “channels first”, not “channels last” like TF. I can’t run TensorFlow in my environment). This is equivalent to numpy.reshape with ‘C’ ordering: ‘C’ means to read / write the elements using C-like index order, with the last axis … Read more
TimeDistributedDense applies a same dense to every time step during GRU/LSTM Cell unrolling. So the error function will be between predicted label sequence and the actual label sequence. (Which is normally the requirement for sequence to sequence labeling problems). However, with return_sequences=False, Dense layer is applied only once at the last cell. This is normally … Read more
That both seem to work doesn’t mean they do the same. Flatten will take a tensor of any shape and transform it into a one dimensional tensor (plus the samples dimension) but keeping all values in the tensor. For example a tensor (samples, 10, 20, 1) will be flattened to (samples, 10 * 20 * … 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.
If you want to get weights and biases of all layers, you can simply use: for layer in model.layers: print(layer.get_config(), layer.get_weights()) This will print all information that’s relevant. If you want the weights directly returned as numpy arrays, you can use: first_layer_weights = model.layers[0].get_weights()[0] first_layer_biases = model.layers[0].get_weights()[1] second_layer_weights = model.layers[1].get_weights()[0] second_layer_biases = model.layers[1].get_weights()[1] etc.