相关疑难解决方法(0)

我试图在删除之前使用Keras提供的代码.这是代码:

def precision(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision

def recall(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    recall = true_positives / (possible_positives + K.epsilon())
    return recall

def fbeta_score(y_true, y_pred, beta=1):
    if beta < 0:
        raise ValueError('The lowest choosable beta is zero (only precision).')

    # If there are no true positives, fix the F score at 0 like …

我是 Tensorflow 领域的新手，正在研究 mnist 数据集分类的简单示例。我想知道除了准确性和损失（并可能显示它们）之外，如何获得其他指标（例如精度、召回率等）。这是我的代码：

from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import TensorBoard
import os 

#load mnist dataset
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

#create and compile the model
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)), 
  tf.keras.layers.Dense(128, activation='relu'), 
  tf.keras.layers.Dropout(0.2), 
  tf.keras.layers.Dense(10, activation='softmax') 
])
model.summary()

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

#model checkpoint (only if there is an improvement)

checkpoint_path = "logs/weights-improvement-{epoch:02d}-{accuracy:.2f}.hdf5"

cp_callback = ModelCheckpoint(checkpoint_path, monitor='accuracy',save_best_only=True,verbose=1, mode='max')

#Tensorboard
NAME = "tensorboard_{}".format(int(time.time())) #name of …