如何在TensorFlow中獲得輸出預測

Question

如標題所示，我試圖在TensorFlow模型中獲取實際的預測。 問題是，即使已經有多個答案，我也不了解如何獲取預測。 我不了解pred.eval或會話函數需要什么數據，我希望這里有人可以解釋它。

我正在使用的代碼在這里：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import matplotlib as plt
tf.logging.set_verbosity(tf.logging.INFO)


def cnn_model_fn(features, labels, mode):
    input_layer=tf.reshape(features["x"], [-1, 28, 28, 1])
    conv1=tf.layers.conv2d(
        inputs=input_layer,
        filters=32,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu
    )
    pool1=tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
    conv2=tf.layers.conv2d(
        inputs=pool1,
        filters=64,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu
    )
    pool2=tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
    pool2_flat=tf.reshape(pool2, [-1, 7*7*64])

    dense=tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)

    dropout=tf.layers.dropout(
        inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN
    )
    logits=tf.layers.dense(inputs=dropout, units=10)

    tf.argmax(input=logits, axis=1)
    tf.nn.softmax(logits, name="softmax_tensor")
    predictions = {
        "classes": tf.argmax(input=logits, axis=1),
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    loss=tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    if mode == tf.estimator.ModeKeys.TRAIN:
        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=mode, loss=loss, train_op=train_op)
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(
            labels=labels, predictions=predictions["classes"])}
    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


def main(unused_argv):
    mnist=tf.contrib.learn.datasets.load_dataset("mnist")
    train_data=mnist.train.images
    train_labels=np.asarray(mnist.train.labels, dtype=np.int32)
    eval_data=mnist.test.images
    eval_labels=np.asarray(mnist.test.labels, dtype=np.int32)

    mnist_classifier=tf.estimator.Estimator(
        model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model"
    )

    tensors_to_log={"probabilities": "softmax_tensor"}
    logging_hook=tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)

    train_input_fn=tf.estimator.inputs.numpy_input_fn(
        x={"x": train_data},
        y=train_labels,
        batch_size=100,
        num_epochs=None,
        shuffle=True
    )
    mnist_classifier.train(
        input_fn=train_input_fn,
        steps=20000,
        hooks=[logging_hook]
    )
    eval_input_fn=tf.estimator.inputs.numpy_input_fn(
        x={"x": eval_data},
        y=eval_labels,
        num_epochs=1,
        shuffle=False
    )
    eval_results=mnist_classifier.evaluate(input_fn=eval_input_fn)
    print(eval_results)

if __name__ == "__main__":
    tf.app.run()`

在這種情況下我該怎么辦？

任何建議表示贊賞，並在此先感謝

Answer 1

假設您要獲取預測的輸入數據稱為predict_data （如果您對此感興趣，可以在這里使用train_data或eval_data ），

pred_input_fn = tf.estimator.inputs.numpy_input_fn(x={'x': predict_data}, shuffle=False)
predictor = list(mnist_classifier.predict(pred_input_fn))

在這一點上， predictor是字典的列表，這些字典將'classes'映射到預測的類別，並將'probabilities'映射到相關的概率。 您可以從中得到的結果正是您在cnn_model_fn指定為predictions的cnn_model_fn 。