Question

正如标题所述，我有一个不会执行的tensorflow ML文件。没有错误消息，也没有结果。终端只是说代码已完成。我正在关注有关https://www.tensorflow.org/tutorials/estimators/cnn

的教程

这是最终代码：

##imports and startup
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)
flags = tf.app.flags
FLAGS = flags.FLAGS
def main(args):
    flags.DEFINE_string('name', None,
                        'Append a name Tag to run.')

    flags.DEFINE_string('hypes', 'hypes/medseg.json',
                        'File storing model parameters.')

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


def cnn_model_fn(features, labels, mode):
    input_layer = tf.reshape(features[x], [-1, 28, 28, 1]) ##reshaping the inputs to [batchsize, height, width, channel]

    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 = pool2.reshape(pool2, [-1, 7*7*64])

    dense = tf.layers.dense(input = 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")
    prediction={
        "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=prediction["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=50,
    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)

终端中的最终响应只是“退出代码为0的处理完成” 我尝试使用多个IDLE，并直接从教程中复制了代码，但没有任何更改。我该怎么办？

Tensorflow代码不执行

0 个答案: