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<p>我有一个简单的Python代码(用于培训的<code>Keras</code>教程)。我尝试删除<code>img = img.convert('L')</code>以在加载图像时保留颜色(我的所有图像都是RGB颜色的,因此数据不是问题所在),但遇到以下错误:</p>
<pre><code>training_images = np.array([i[0] for i in training_data]).reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 3)
ValueError: could not broadcast input array from shape (300,300,3) into shape (300,300)
</code></pre>
<p>怎么了?如何修复?你知道吗</p>
<pre><code>from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from PIL import Image
from random import shuffle, choice
import numpy as np
import os
IMAGE_SIZE = 300
IMAGE_DIRECTORY = './data/test_set'
def label_img(name):
if name == 'cats': return np.array([1, 0])
elif name == 'notcats' : return np.array([0, 1])
def load_data():
train_data = []
directories = next(os.walk(IMAGE_DIRECTORY))[1]
for dirname in directories:
file_names = next(os.walk(os.path.join(IMAGE_DIRECTORY, dirname)))[2]
for i in range(200):
image_name = choice(file_names)
image_path = os.path.join(IMAGE_DIRECTORY, dirname, image_name)
label = label_img(dirname)
img = Image.open(image_path)
#img = img.convert('L')
img = img.resize((IMAGE_SIZE, IMAGE_SIZE), Image.ANTIALIAS)
train_data.append([np.array(img), label])
return train_data
def create_model():
model = Sequential()
model.add(Conv2D(32, kernel_size = (3, 3), activation='relu',
input_shape=(IMAGE_SIZE, IMAGE_SIZE, 1)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(64, activation='relu'))
model.add(Dense(2, activation = 'softmax'))
return model
training_data = load_data()
training_images = np.array([i[0] for i in training_data]).reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 1)
training_labels = np.array([i[1] for i in training_data])
model = create_model()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(training_images, training_labels, batch_size=50, epochs=10, verbose=1)
</code></pre>