Adding to @Dmitry Kabanov, they are similar yet they aren’t exactly the same thing. If you care about performance, need to look in to critical differences between them. model.predict model(x) loops over the data in batches which means means that predict() calls can scale to very large arrays. happens in-memory and doesn’t scale not differentiable … 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
When you saved your model using: old_model.save(‘my_model.h5’) it will save following: The architecture of the model, allowing to create the model. The weights of the model. The training configuration of the model (loss, optimizer). The state of the optimizer, allowing training to resume from where you left before. So then, when you load the model: … Read more
use this from tensorflow.keras.utils import to_categorical instead of from keras.utils import to_categorical
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
In Tensoflow2 there is no need to perform from tensorflow import set_random_seed in order to run set_random_seed(x) (as it was in older version) Only have to run import tensorflow tensorflow.random.set_seed(x) Thanks to @David Buck
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).
it may be because type of weight initialization you are using otherwise this should not happen try with same initializer in both the models
NOTE: this answer is outdated and only works with TF1. Check @bers’s answer for a solution tested on TF2. After model compilation, the placeholder tensor for y_true is in model.targets and y_pred is in model.outputs. To save the values of these placeholders at each batch, you can: First copy the values of these tensors into … Read more
[Answer after the question was edited & clarified:] Before rushing into implementation issues, it is always a good practice to take some time to think about the methodology and the task itself; arguably, intermingling early stopping with the cross validation procedure is not a good idea. Let’s make up an example to highlight the argument. … Read more