def get_train_inputs(batch_size, mnist_data): """Return the input function to get the training data. Args: batch_size (int): Batch size of training iterator that is returned by the input function. mnist_data (Object): Object holding the loaded mnist data. Returns: (Input function, Ite...
defget_train_inputs(batch_size, mnist_data): """Return the input function to get the training data. Args: batch_size (int): Batch size of training iterator that is returned by the input function. mnist_data (Object): Object holding the loaded mnist data. Returns: (Input function, Itera...
w1= graph.get_tensor_by_name("w1:0") w2= graph.get_tensor_by_name("w2:0") feed_dict= {w1: 13.0, w2: 17.0}#接下来,访问你想要执行的opop_to_restore = graph.get_tensor_by_name("op_to_restore:0")#在当前图中能够加入opadd_on_op = tf.multiply(op_to_restore, 2)print(sess.run...
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) ckpt = tf.train.get_checkpoint_state("/home/xsr-ai/study/mnist/mnist-model") saver.restore(sess, ckpt.all_model_checkpoint_paths[0]...
}defget_network_fn(name, num_classes, weight_decay=0.0, is_training=False):"""Returns a network_fn such as `logits, end_points = network_fn(images)`. Args: name: The name of the network. num_classes: The number of classes to use for classification. ...
correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y)) # Calculate accuracy on the test set accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print ("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train})) print ("Test Accuracy:", accuracy.eval({X: X_...
correct_prediction=tf.equal(y_conv_labels,tf.argmax(y_,1))# 用平均值来统计测试准确率 accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32),name="accuracy")defconvert2onehot(data):# covert data to onehot representationreturnpd.get_dummies(data)file_path="./1CNN/mnistcnn/datas/...
prediction = tf.round(tf.nn.sigmoid(final_output)) predictions_correct = tf.cast(tf.equal(prediction, y_target), tf.float32) accuracy = tf.reduce_mean(predictions_correct) 我们现在准备开始我们的训练循环。我们将训练 1500 代并保存模型损失并训练和测试精度以便以后进行绘图。我们的训练循环使用以下...
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print("Accuracy:", accuracy.eval({X: x_test, Y: y_test_new})) # 计算模型预测 y_test_pred = sess.run(pred, feed_dict={X: x_test}) y_pred = np.empty((y_test_pred.shape[0]), dtype='int32') ...
mode:一个ModeKeys,指定是training(训练)、evaluation(计算)还是prediction(预测). predictions:PredictionsTensoror dict ofTensor. loss:Training lossTensor. Must be either scalar, or with shape[1]. train_op:适用于训练的步骤. eval_metric_ops: Dict of metric results keyed by name. The values of the...