我花了一些令人沮丧的时间来编写一个简单的模型导出和导入与GANEstimator但我无法让它工作。基本上它是带有GANEstimator的条件GAN，最后4个LOC是导出。最后一行抛出：
ValueError: export_outputs must be a dict and not<class 'NoneType'>
如果有人能看一眼，我真的很感激。正如我所说，我只是想训练模型并导出它，并在另一个python脚本中，我想重新加载模型（而不是说生成器）并以某种方式输入数据。这是我的代码：
import tensorflow as tf
#import tensorflow.contrib.eager as tfe
#tfe.enable_eager_execution()
tfgan = tf.contrib.gan
slim = tf.contrib.slim
layers = tf.contrib.layers
ds = tf.contrib.distributions
import time
import datasets.download_and_convert_mnist as download_and_convert_mnist
from mnist import data_provider,util
import os
import matplotlib.pyplot as plt
import numpy as np
import scipy
from common import *
MODEL_FILE_NAME = 'cond_garden_experimental'
MODEL_SAVE_PATH = os.path.join(MODEL_SAVE_PATH, MODEL_FILE_NAME)
IMGS_SAVE_PATH = os.path.join(IMGS_SAVE_PATH, MODEL_FILE_NAME)
#constants and variables
num_epochs = 2000
batch_size = 32
latent_dims = 64
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
def _get_shape(tensor):
tensor_shape = array_ops.shape(tensor)
static_tensor_shape = tensor_util.constant_value(tensor_shape)
return (static_tensor_shape if static_tensor_shape is not None else
tensor_shape)
def condition_tensor(tensor, conditioning):
tensor.shape[1:].assert_is_fully_defined()
num_features = tensor.shape[1:].num_elements()
mapped_conditioning = layers.linear(
layers.flatten(conditioning), num_features)
print(mapped_conditioning.shape)
if not mapped_conditioning.shape.is_compatible_with(tensor.shape):
mapped_conditioning = array_ops.reshape(
mapped_conditioning, _get_shape(tensor))
return tensor + mapped_conditioning
def conditional_discriminator_fn(img, inputs, weight_decay=2.5e-5):
one_hot_labels = inputs[1]
with slim.arg_scope(
[layers.conv2d, layers.fully_connected],
activation_fn=leaky_relu, normalizer_fn=None,
weights_regularizer=layers.l2_regularizer(weight_decay),
biases_regularizer=layers.l2_regularizer(weight_decay)):
net = layers.conv2d(img, 64, [4, 4], stride=2)
net = layers.conv2d(net, 128, [4, 4], stride=2)
net = layers.flatten(net)
net = condition_tensor(net, one_hot_labels)
net = layers.fully_connected(net, 1024, normalizer_fn=layers.batch_norm)
return layers.linear(net, 1)
leaky_relu = lambda net: tf.nn.leaky_relu(net, alpha=0.01)
global_noise = None
global_condition = None
def conditional_generator_fn(inputs, weight_decay=2.5e-5):
if isinstance(inputs,dict):
noise, one_hot_labels = inputs['noise'],inputs['condition']
else:
noise, one_hot_labels = inputs
global global_noise
global global_condition
global_noise = noise
global_condition = one_hot_labels
with slim.arg_scope(
[layers.fully_connected, layers.conv2d_transpose],
activation_fn=tf.nn.relu, normalizer_fn=layers.batch_norm,
weights_regularizer=layers.l2_regularizer(weight_decay)):
net = layers.fully_connected(noise, 1024)
net = condition_tensor(net, one_hot_labels)
net = layers.fully_connected(net, 7 * 7 * 128)
net = tf.reshape(net, [-1, 7, 7, 128])
net = layers.conv2d_transpose(net, 64, [4, 4], stride=2)
net = layers.conv2d_transpose(net, 32, [4, 4], stride=2)
# Make sure that generator output is in the same range as `inputs`
# ie [-1, 1].
net = layers.conv2d(net, 1, 4, normalizer_fn=None, activation_fn=tf.tanh)
return net
def _get_train_input_fn(batch_size, noise_dims, dataset_dir=None, num_threads=4):
def train_input_fn():
with tf.device('/cpu:0'):
images, labels, _ = data_provider.provide_data('train', batch_size, dataset_dir, num_threads=num_threads)
noise = tf.random_normal([batch_size, noise_dims])
return ((noise,labels),images)
return train_input_fn
def _get_predict_input_fn():
def predict_input_fn(params):
noise,condition = params['noise'],params['condition']
noise_tensor = tf.convert_to_tensor(noise)
condition_tensor = tf.convert_to_tensor(condition)
#with tf.device('/cpu:0'):
# images, condition_tensor, _ = data_provider.provide_data('train', batch_size, MNIST_DATA_DIR, num_threads=4)
# noise_tensor = tf.random_normal([batch_size, latent_dims])
print(noise_tensor.shape,condition_tensor.shape)
return ((noise_tensor,condition_tensor),None)
return predict_input_fn
def visualize_training_generator(train_step_num, start_time, data_np):
"""Visualize generator outputs during training.
Args:
train_step_num: The training step number. A python integer.
start_time: Time when training started. The output of `time.time()`. A
python float.
data: Data to plot. A numpy array, most likely from an evaluated TensorFlow
tensor.
"""
print('Training step: %i' % train_step_num)
time_since_start = (time.time() - start_time) / 60.0
print('Time since start: %f m' % time_since_start)
print('Steps per min: %f' % (train_step_num / time_since_start))
plt.axis('off')
plt.imshow(np.squeeze(data_np), cmap='gray')
plt.savefig(os.path.join(IMGS_SAVE_PATH,MODEL_FILE_NAME+str(train_step_num)+'.png'))
if __name__ == '__main__':
setup_clean_directory(MODEL_SAVE_PATH)
setup_clean_directory(IMGS_SAVE_PATH)
#prepare data
download_and_convert_mnist.run(MNIST_DATA_DIR)
with tf.device('/cpu:0'):
images, one_hot_labels, _ = data_provider.provide_data('train', batch_size, MNIST_DATA_DIR)
gan_estimator = tfgan.estimator.GANEstimator(
MODEL_SAVE_PATH,
generator_fn=conditional_generator_fn,
discriminator_fn=conditional_discriminator_fn,
generator_loss_fn=tfgan.losses.wasserstein_generator_loss,
discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,
generator_optimizer=tf.train.AdamOptimizer(0.001, 0.5),
discriminator_optimizer=tf.train.AdamOptimizer(0.0001, 0.5),
add_summaries=tfgan.estimator.SummaryType.IMAGES)
train_input_fn = _get_train_input_fn(batch_size, noise_dims=latent_dims, dataset_dir=MNIST_DATA_DIR)
gan_estimator.train(train_input_fn, max_steps=1)
#try 1
from tensorflow.python.estimator.export import export
feat_dict = {'noise':global_noise,'condition':global_condition}
sirf = export.build_raw_serving_input_receiver_fn(feat_dict)
gan_estimator.export_savedmodel(EXPORT_DIR_ROOT, sirf)
答案 0 :(得分：5)
2018-5-09 ：目前GANEstimator没有创建上述export_output字典;可以通过在第162行之后查看tensorflow/contrib/gan/python/estimator/python/head_impl.py来验证它。
您可以通过此GitHub Pull Request跟踪此问题的状态。
model_fn需要使用EstimatorSpec字典返回export_outputs。有点像：
if mode == Modes.PREDICT:
predictions = {
'classes': tf.gather(label_values, predicted_indices),
'scores': tf.reduce_max(probabilities, axis=1)
}
export_outputs = {
'prediction': tf.estimator.export.PredictOutput(predictions)
}
return tf.estimator.EstimatorSpec(
mode, predictions=predictions, export_outputs=export_outputs)