如何在 tensorflow MNIST 模型上測試單個圖像？

Question

我已經按照 tensorflow 教程中的教程構建了一個用於手寫數字識別的MNIST 模型。 我想通過向分類器輸入單個圖像並獲得它預測的輸出來測試模型。

這是分類器的完整代碼。

我已經嘗試使用 imread 讀取圖像，但沒有奏效

 from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf
import pylab
import os
from scipy.misc import imread
import matplotlib.image as mpimg
import matplotlib.pyplot 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,
        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)

    predictions = {
    "classes":tf.argmax(input=logits, axis=1),
    "probabilites": 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):
  # Load training and eval data
  mnist = tf.contrib.learn.datasets.load_dataset("mnist")
  train_data = mnist.train.images  # Returns np.array
  train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
  eval_data = mnist.test.images  # Returns np.array
  eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

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

  Set up logging for predictions
  Log the values in the "Softmax" tensor with label "probabilities"
  tensors_to_log = {"probabilities": "softmax_tensor"}
  logging_hook = tf.train.LoggingTensorHook(
      tensors=tensors_to_log, every_n_iter=50)

  # Train the model
  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])

  # Evaluate the model and print results
  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)   

Answer 1

我發現一個非常有用的資源是 Tensorflow 的官方教程，其中包括他們的 Estimator 的演示。 在其中，您可以找到如何使用模型測試 numpy 數組。 這是鏈接：https ://github.com/tensorflow/models/tree/master/official/mnist

希望這可以幫助！