PHP CodeSniffer:错误:指定的嗅探代码“ Generic.Files.LineEndings.InvalidEOLChar”无效

时间:2019-03-11 09:31:01

标签: codesniffer phpcs phpcodesniffer

我尝试排除Windows计算机上EOL字符检查的尝试总是导致此错误消息:

>vendor\bin\phpcs.bat --standard=PSR2 --exclude=Generic.Files.LineEndings.InvalidEOLChar src\version.php
ERROR: The specified sniff code "Generic.Files.LineEndings.InvalidEOLChar" is invalid

Run "phpcs --help" for usage information

不知道我在做什么错。我已经通过composer安装了PHP CodeSniffer,并且正在运行3.4.0版。

1 个答案:

答案 0 :(得分:1)

train_fnames, train_labels, test_fnames, test_labels =\ spec_to_paths_and_labels('count_data/spec.csv') train_fnames = 'count_data/' + train_fnames test_fnames = 'count_data/' + test_fnames def read_npy_file(item): data = np.load(item.decode()) return data.astype(np.int32) # gdsii_placeholder = tf.placeholder(tf.float32, shape=(None, 224, 224, 1)) # label_placeholder = tf.placeholder(tf.int32, shape=[1]) def cnn_model(features, labels, mode): conv1 = tf.layers.conv2d(features, filters=32, kernel_size=(5, 5)) pool1 = tf.layers.max_pooling2d(conv1, pool_size=(2, 2), strides=2) conv2 = tf.layers.conv2d(pool1, filters=64, kernel_size=(5, 5)) pool2 = tf.layers.max_pooling2d(conv2, pool_size=(2, 2), strides=2) flat = tf.layers.flatten(pool2) dense = tf.layers.dense(flat, units=1024, activation='relu') dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=True) logits = tf.layers.dense(inputs=dropout, units=10) predictions = { "classes": tf.argmax(input=logits, axis=1), "probabilities": tf.nn.softmax(logits, name="softmax_tensor") } loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001) train_op = optimizer.minimize( loss=loss, global_step=tf.train.get_global_step()) return tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op) def main(_): # load the dataset data = tf.data.Dataset.from_tensor_slices(train_fnames) data = data.map(lambda item: tuple(tf.py_func( read_npy_file, [item], [tf.int32, ]))) gdsii_classifier = tf.estimator.Estimator( model_fn=cnn_model, model_dir="/tmp/gdsii_classifier") res = gdsii_classifier.evaluate(lambda: data) print(res) if __name__ == "__main__": tf.app.run() CLI参数接受三部分的嗅探代码,但是您传入了四部分的错误代码。

在您的情况下,嗅探代码为--exclude,并且该嗅探仅生成一个错误代码,因此您可以忽略整个嗅探:

Generic.Files.LineEndings

如果要排除单个错误代码,或者仅想锁定项目的标准,则需要使用ruleset.xml文件:https://github.com/squizlabs/PHP_CodeSniffer/wiki/Annotated-Ruleset