numpy-ndarray

问题 I'm trying to replicate the following batch generator for a project. However I'm having issues reshaping my data. The goal of the function is to take an array of [6000,3000] and reshape it to shape [batch_size, 100,3000,1]. Functioning code I'm trying to replicate def gen(dict_files, aug=False): while True: record_name = random.choice(list(dict_files.keys())) batch_data = dict_files[record_name] all_rows = batch_data['x'] for i in range(batch_size): start_index = random.choice(range(all_rows

问题 I'm trying to replicate the following batch generator for a project. However I'm having issues reshaping my data. The goal of the function is to take an array of [6000,3000] and reshape it to shape [batch_size, 100,3000,1]. Functioning code I'm trying to replicate def gen(dict_files, aug=False): while True: record_name = random.choice(list(dict_files.keys())) batch_data = dict_files[record_name] all_rows = batch_data['x'] for i in range(batch_size): start_index = random.choice(range(all_rows

问题 Wokring on a cGAN as I have mentioned over the last few questions I posted. The code lines giving me the error, which I think I have narrowed down perfectly is: for images, labels in train_dataset: #gen_loss, disc_loss = train_step(images, labels) The original Code for MNIST represents images as something like: [-1.], [-1.], [-1.]], [[-1.], [-1.], [-1.], ..., [-1.], [-1.], [-1.]], [[-1.], [-1.], [-1.], ..., [-1.], [-1.], [-1.]]]], dtype=float32)>, <tf.Tensor: id=3685, shape=(200, 28, 28, 1),

问题 I have a numpy array of these dimensions data.shape (categories, models, types, events): (10, 11, 50, 100) Now I want to do sample with replacement in the innermost array (100) only. For a single array such as this: data[0][0][0] array([ 40.448624 , 39.459843 , 33.76762 , 38.944622 , 21.407362 , 35.55499 , 68.5111 , 16.512974 , 21.118315 , 18.447166 , 16.026619 , 21.596252 , 41.798622 , 63.01645 , 46.886642 , 68.874756 , 17.472408 , 53.015724 , 85.41213 , 59.388977 , 17.352108 , 61.161705 ,

问题 I try to use numpy with nptyping 's Array to do my typehinting. I tried the following: enemy_hand: Array[float, 48] = np.zeros(48) I get an typerror: Expected Type 'Array[float, Any]', got 'ndarray' instead as far as I understand from this: https://pypi.org/project/nptyping/ thats how it should look. 回答1: nptyping is currently useless for static analysis. Quoting a post by the library's developer on its issue tracker, mypy is just not supported by nptyping (yet) I wouldn't put much hope in

问题 I try to use numpy with nptyping 's Array to do my typehinting. I tried the following: enemy_hand: Array[float, 48] = np.zeros(48) I get an typerror: Expected Type 'Array[float, Any]', got 'ndarray' instead as far as I understand from this: https://pypi.org/project/nptyping/ thats how it should look. 回答1: nptyping is currently useless for static analysis. Quoting a post by the library's developer on its issue tracker, mypy is just not supported by nptyping (yet) I wouldn't put much hope in

问题 I try to use numpy with nptyping 's Array to do my typehinting. I tried the following: enemy_hand: Array[float, 48] = np.zeros(48) I get an typerror: Expected Type 'Array[float, Any]', got 'ndarray' instead as far as I understand from this: https://pypi.org/project/nptyping/ thats how it should look. 回答1: nptyping is currently useless for static analysis. Quoting a post by the library's developer on its issue tracker, mypy is just not supported by nptyping (yet) I wouldn't put much hope in

问题 I have got a X_train np.array with shape of (1433, 1) . The first dimension ( 1433 ) is the number of images for training. The second dimension ( 1 ) is an np.array which itself has a shape (224, 224, 3) . I could confirm it by X_train[0][0].shape . I need to fit X_train to the model: model.fit([X_train, y_train[:,1:]], y_train[:,0], epochs=50, batch_size=32, verbose=1) Error output is self-explanatory: Traceback (most recent call last): File "/home/combined/file_01.py", line 97, in <module>

问题 This question already has answers here : python abstractmethod with another baseclass breaks abstract functionality (2 answers) Closed 22 days ago . I am trying to create a python (3.5) class structure where I use abstract methods to indicate which methods should be implemented. The following works as expected: from abc import ABC, abstractmethod class Base(ABC): @abstractmethod def foo(self): pass class Derived(Base, object): # Inheriting from object probably is superfluous (?) pass Derived(

问题 This question already has answers here : python abstractmethod with another baseclass breaks abstract functionality (2 answers) Closed 22 days ago . I am trying to create a python (3.5) class structure where I use abstract methods to indicate which methods should be implemented. The following works as expected: from abc import ABC, abstractmethod class Base(ABC): @abstractmethod def foo(self): pass class Derived(Base, object): # Inheriting from object probably is superfluous (?) pass Derived(