Question

我已经训练了用于MNIST数字识别的cnn模型...现在我想为我的模型提供我自己的输入并期望带有标签名称的正确输出... 我不知道如何为我训练过的模型提供输入，任何人都可以帮助我....这是我的全部代码......

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

import numpy as np
import tensorflow as tf

from tensorflow.contrib import learn
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib

tf.logging.set_verbosity(tf.logging.INFO)
sess = tf.Session()


def cnn_model_fn(features, labels, mode):

  input_layer = tf.reshape(features, [-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 == learn.ModeKeys.TRAIN)

  logits = tf.layers.dense(inputs=dropout, units=10)

  loss = None
  train_op = None

  if mode != learn.ModeKeys.INFER:
    onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
    loss = tf.losses.softmax_cross_entropy(
        onehot_labels=onehot_labels, logits=logits)

  # Configure the Training Op (for TRAIN mode)
  if mode == learn.ModeKeys.TRAIN:
    train_op = tf.contrib.layers.optimize_loss(
        loss=loss,
        global_step=tf.contrib.framework.get_global_step(),
        learning_rate=0.001,
        optimizer="SGD")

  # Generate Predictions
  predictions = {
      "classes": tf.argmax(
          input=logits, axis=1),
      "probabilities": tf.nn.softmax(
          logits, name="softmax_tensor")
  }

  # Return a ModelFnOps object
  return model_fn_lib.ModelFnOps(
      mode=mode, predictions=predictions, loss=loss, train_op=train_op)


def main(unused_argv):
  # Load training and eval data
  mnist = 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 = learn.Estimator(
      model_fn= cnn_model_fn, model_dir="/home/kumar/Downloads/")

  # 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
  mnist_classifier.fit(
      x=train_data,
      y=train_labels,
      batch_size=100,
      steps=20000,
      monitors=[logging_hook])

# Configure the accuracy metric for evaluation
  metrics = {
      "accuracy":
          learn.MetricSpec(
              metric_fn=tf.metrics.accuracy, prediction_key="classes"),
  }

  # Evaluate the model and print results
  eval_results = mnist_classifier.evaluate(
      x=eval_data, y=eval_labels, metrics=metrics)
  print(eval_results)

  saver = tf.train.Saver()
  saver.restore(sess, "/home/kumar/Downloads/model_simple.ckpt")
  print("model restored.")


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

Answer 1

修改

使用predict输入数据并获取预测标签。

mnist_classifier.fit(your_data)    # your_data should have shape = (number_of_sample, number_of_features)

请参阅document。

如何用cnn训练模型预测输入图像

1 个答案:

修改