Image anomaly detection using Variational Autoencoder Part 2 (Shiojiri ML Mokumokukai # 7)
We would like to detect anomalies using VAE and the original data set by referring to the code of the site at the above URL.
-------------------------------------------------- ----------------------- AttributeError Traceback (most recent call last)<ipython-input-37-02dbf7abd247>in<module> 2 if y_train [i] == 1: # 7 for sneakers 3 temp = x_train [i,:,:,:] ---->4 x_train_b.numpy.append (temp.reshape ((x_train_shape ,], x_train_shape , x_train_shape )))) Five 6 x_train_b = np.array (x_train_b) AttributeError:'numpy.ndarray' object has no attribute'numpy'
I get the above error in the middle of the code and I don't know how to improve the code.
What i am trying
from __future__ import absolute_import from __future__ import division from __future__ import print_function from keras.layers import Lambda, Input, Dense, Reshape from keras.models import Model from keras.losses import mse from keras.utils import plot_model from keras import backend as K from keras.layers import BatchNormalization, Activation, Flatten from keras.layers.convolutional import Conv2DTranspose, Conv2D import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as colors import os from sklearn import metrics os.chdir ('/ Users/user_name/desktop/VAE') os.getcwd () def result_score (model, x, name, height = 80, width = 80, move = 2): score =  for k in range (len (x)): max_score = -1000000000if k% 100 == 0: print (k) for i in range (int ((x.shape  -height)/move) +1): for j in range (int ((x.shape  -width)/move) +1): x_sub = x [k, i * move: i * move + height, j * move: j * move + width, 0] x_sub = x_sub.reshape (1, height, width, 1) #Conventional method if name == "old_": #Score temp_score = model.evaluate (x_sub, batch_size = 1, verbose = 0) if temp_score>max_score: max_score = temp_score #Proposed method else: else: #Score mu, sigma = model.predict (x_sub, batch_size = 1, verbose = 0) loss = 0 for o in range (height): for l in range (width): loss + = 0.5 * (x_sub [0, o, l, 0] --mu [0, o, l, 0]) ** 2/sigma [0, o, l, 0] if loss>max_score: max_score = loss score.append (max_score) return (score) def cut_img (x, number, height = 224, width = 224): print ("cutting images ...") x_out =  x_shape = x.shape for i in range (number): shape_0 = np.random.randint (0, x_shape ) shape_1 = np.random.randint (0, x_shape -height) shape_2 = np.random.randint (0, x_shape -width) temp = x [shape_0, shape_1: shape_1 + height, shape_2: shape_2 + width, 0] x_out.append (temp.reshape ((height, width, x_shape ))) print ("Complete.") x_out = np.array (x_out) return x_out # reparameterization trick # instead of sampling from Q (z | X), sample eps = N (0, I) # z = z_mean + sqrt (var) * eps def sampling (args):z_mean, z_log_var = args batch = K.shape (z_mean)  dim = K.int_shape (z_mean)  # by default, random_normal has mean = 0 and std = 1.0 epsilon = K.random_normal (shape = (batch, dim)) return z_mean + K.exp (0.5 * z_log_var) * epsilon # dataset from bcn_dataset import BCN_Dataset2 (x_train, y_train), (x_test, y_test) = BCN_Dataset2.create_bcn () x_train = x_train.reshape (x_train.shape , 224, 224, 3) x_test = x_test.reshape (x_test.shape , 224, 224, 3) x_train = x_train.astype ('float32')/255 x_test = x_test.astype ('float32')/255 x_train_b =  x_test_b =  x_test_n =  x_train_shape = x_train.shape for i in range (len (x_train)): if y_train [i] == 1: # 7 for sneakers temp = x_train [i,:,:,:] x_train_b.numpy.append (temp.reshape ((x_train_shape ,], x_train_shape , x_train_shape )))) x_train_b = np.array (x_train_b) x_train_b = cut_img (x_train_b, 50) print ("train data:", len (x_train_b))
'numpy.ndarray' object has no attribute'append' error
Last time, I asked a similar question, and the respondent said, "
append ()There is no method.
numpy.append ()There is a method of. I tried the code by trial and error (such as adding numpy before append), but it didn't work and the same error occurred. I'm worried about my head.
I'm very sorry if you have to change all the code or if the dataset you created is created incorrectly, but any small thing is fine, so please give me some advice.
My PC is macOS Catalina version 10.15.5
Python version is 3.6.5
I'm using a Jupiter notebook
Answer # 1
Since numpy.append is a function, its usage is as follows.
numpy.append (original array, target array)
Since it is a function, it has a return value. So, please define the return value with some variable and perform the subsequent processing.
Answer # 2
'numpy.ndarray' object has no attribute'numpy'
'numpy.ndarray' does not have an attribute called'numpy'.
What's in x_train_b?
Answer # 3
import numpy as npSo in the code
numpyWhen you want to call
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