執(zhí)行以下代碼時出現(xiàn)上述錯誤。我正在嘗試在下面的 tensorflow 神經(jīng)網(wǎng)絡(luò)實現(xiàn)教程中解決這個問題。 https://www.datacamp.com/community/tutorials/tensorflow-tutorialdef load_data(data_directory):directories = [d for d in os.listdir(data_directory) if os.path.isdir(os.path.join(data_directory, d))]labels = []images = []for d in directories: label_directory = os.path.join(data_directory, d) file_names = [os.path.join(label_directory, f) for f in os.listdir(label_directory) if f.endswith(".ppm")] for f in file_names: images.append(skimage.data.imread(f)) labels.append(int(d))return images, labelsimport osimport skimagefrom skimage import transformfrom skimage.color import rgb2grayimport numpy as npimport kerasfrom keras import layersfrom keras.layers import DenseROOT_PATH = "C://Users//Jay//AppData//Local//Programs//Python//Python37//Scriptcodes//BelgianSignals"train_data_directory = os.path.join(ROOT_PATH, "Training")test_data_directory = os.path.join(ROOT_PATH, "Testing")images, labels = load_data(train_data_directory)# Print the `labels` dimensionsprint(np.array(labels))# Print the number of `labels`'s elementsprint(np.array(labels).size)# Count the number of labelsprint(len(set(np.array(labels))))# Print the `images` dimensionsprint(np.array(images))# Print the number of `images`'s elementsprint(np.array(images).size)# Print the first instance of `images`np.array(images)[0]images28 = [transform.resize(image, (28, 28)) for image in images]images28 = np.array(images28)images28 = rgb2gray(images28)# Import `tensorflow` import tensorflow as tf # Initialize placeholders x = tf.placeholder(dtype = tf.float32, shape = [None, 28, 28])y = tf.placeholder(dtype = tf.int32, shape = [None])# Flatten the input dataimages_flat = tf.keras.layers.flatten(x)# Fully connected layer logits = tf.contrib.layers.dense(images_flat, 62, tf.nn.relu)
2 回答
吃雞游戲
TA貢獻1829條經(jīng)驗 獲得超7個贊
任何一個
from keras.layers import Flatten
并使用
Flatten()(input)
或者
簡單地使用
tf.keras.layers.Flatten()(input)
躍然一笑
TA貢獻1826條經(jīng)驗 獲得超6個贊
新的（“keras 作為默認 API”）方法會讓你使用 keras 層，tf.keras.layers.Flatten但你似乎錯過了一些細微差別（評論中沒有提到）。
tf.keras.layers.Flatten() 實際上返回一個 keras 層（可調(diào)用）對象，該對象又需要與您的前一層一起調(diào)用。
所以更像是這樣的：
# Flatten the input data
flatten_layer = tf.keras.layers.Flatten()
images_flat = flatten_layer(x)
或者，為簡潔起見，只是：
# Flatten the input data
images_flat = tf.keras.layers.Flatten()(x)
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