Das deutsche Python-Forum

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beispiel_bilder = !find($train_image_files_path -type f -name "*.jpg"

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import cv2
import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
#matplotlib inline

# image processing
from keras.preprocessing import image as image_utils
from keras.preprocessing.image import img_to_array

# build your own nets
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from keras.optimizers import RMSprop
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU

train_image_files_path = "c://Python35/workspace/fruits/fruits-360/Training/"
valid_image_files_path = "c://Python35/workspace/fruits/fruits-360/Test/"

fruit_list = ["Kiwi", "Banana", "Apricot", "Avocado", "Cocos", "Clementine", "Mandarine", "Orange",
              "Limes", "Lemon", "Peach", "Plum", "Raspberry", "Strawberry", "Pineapple", "Pomegranate"]

output_n = len(fruit_list)
img_width = 20
img_height = 20
channels = 3

batch_size = 32
epochs = 5

beispiel_bilder = !find($train_image_files_path -type f -name "*.jpg"

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beispiel_bilder = list(pathlib.Path(train_image_files_path).rglob("*.jpg"))