TensorFlow——keras--报错+修改

1

mnist.load_data() 报错解决

#导入数据集 fashion_mnist = keras.datasets.fashion_mnist (x_train_all, y_train_all),(x_test, y_test) = fashion_mnist.load_data()

2

Tensorflow2.0报错：ProfilerNotRunningError: Cannot stop profiling. No profiler is running.

#TensorBoard（文件夹）, EarlyStopping, ModelCheckpoint（文件名） #logdir = './callbacks' #定义文件夹 logdir = os.path.join("callbacks") if not os.path.exists(logdir): os.mkdir(logdir) #如果没有该文件，创建文件 output_model_file = os.path.join(logdir, "fashion_mnist_model.h5") #输出的文件也放在该文件夹下 callbacks = [ keras.callbacks.TensorBoard(logdir), keras.callbacks.ModelCheckpoint(output_model_file, save_best_only = True), #保存最好的模型，否则最近的模型 keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3), ] # EarlyStopping的参数： # monitor：关注的指标，一般是目标函数的值 # min_delta：预值，如果比预值低，就停 # patience：当前后两次目标函数的值比min_delta小多少次时，关掉 history = model.fit(x_train_scaled, y_train, epochs=10, validation_data=(x_valid_scaled, y_valid), callbacks = callbacks) # epochs： 迭代10次 ; validation：验证

修改

#logdir = './callbacks' #定义文件夹 logdir = os.path.join("callbacks")

3

报错：AssertionError: Bad argument number for Name: 3, expecting 4 修改：

pip install gast==0.2.2 #0.3.2版本的问题，降级后可以

4

ValueError: Protocol message Features has no “features” field.

def serialize_example(x, y): """Converts x, y to tf.train.Example and serialize""" input_features = tf.train.FloatList(value = x) label = tf.train.FloatList(value = y) features = tf.train.Features( features = { "input_features": tf.train.Feature( float_list = input_features), "label": tf.train.Feature(float_list = label) } ) example = tf.train.Example(features =features) return example.SerializeToString()

修改：取消features中的s

feature = { "input_features": tf.train.Feature( float_list = input_features), "label": tf.train.Feature(float_list = label) }

5

报错：inflate() failed with error -3: incorrect header check [Op:IteratorGetNextSync]

dataset = filename_dataset.interleave( lambda filename: tf.data.TFRecordDataset( filename, compression_type = "GZIP"), cycle_length = n_readers )

修改为：

dataset = dataset.interleave( lambda filename: tf.data.TFRecordDataset( filename, compression_type = "GZIP"), cycle_length = n_readers )