这里是一个新手张量流问题。我正在使用Google QuickDraw数据集进行项目。我使用Google提供的代码来使用数据训练CNN模型。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import ast
import functools
import sys
import tensorflow as tf
tf.logging.set_verbosity('INFO')
def get_num_classes():
with open(FLAGS.classes_path) as label_class:
classes = label_class.readlines()
classes = [x.strip() for x in classes]
return len(classes)
def get_input_fn(mode, tfrecord_pattern, batch_size):
"""Creates an input_fn that stores all the data in memory.
Args:
mode: one of tf.contrib.learn.ModeKeys.{TRAIN, INFER, EVAL}
tfrecord_pattern: path to a TF record file created using create_dataset.py.
batch_size: the batch size to output.
Returns:
A valid input_fn for the model estimator.
"""
def _parse_tfexample_fn(example_proto, mode):
"""Parse a single record which is expected to be a tensorflow.Example."""
feature_to_type = {
"drawing": tf.VarLenFeature(dtype=tf.float32),
"shape": tf.FixedLenFeature([2], dtype=tf.int64)
}
if mode != tf.estimator.ModeKeys.PREDICT:
# The labels won't be available at inference time, so don't add them
# to the list of feature_columns to be read.
feature_to_type["class_index"] = tf.FixedLenFeature([1], dtype=tf.int64)
parsed_features = tf.parse_single_example(example_proto, feature_to_type)
labels = None
if mode != tf.estimator.ModeKeys.PREDICT:
labels = parsed_features["class_index"]
parsed_features["drawing"] = tf.sparse_tensor_to_dense(parsed_features["drawing"])
return parsed_features, labels
def _input_fn():
"""Estimator `input_fn`.
Returns:
A tuple of:
- Dictionary of string feature name to `Tensor`.
- `Tensor` of target labels.
"""
dataset = tf.data.TFRecordDataset.list_files(tfrecord_pattern)
if mode == tf.estimator.ModeKeys.TRAIN:
dataset = dataset.shuffle(buffer_size=10)
dataset = dataset.repeat()
# Preprocesses 10 files concurrently and interleaves records from each file.
dataset = dataset.interleave(
tf.data.TFRecordDataset,
cycle_length=10,
block_length=1)
dataset = dataset.map(
functools.partial(_parse_tfexample_fn, mode=mode),
num_parallel_calls=10)
dataset = dataset.prefetch(10000)
if mode == tf.estimator.ModeKeys.TRAIN:
dataset = dataset.shuffle(buffer_size=1000000)
# Our inputs are variable length, so pad them.
dataset = dataset.padded_batch(
batch_size, padded_shapes=dataset.output_shapes)
features, labels = dataset.make_one_shot_iterator().get_next()
return features, labels
return _input_fn
def model_fn(features, labels, mode, params):
"""Model function for RNN classifier.
This function sets up a neural network which applies convolutional layers (as
configured with params.num_conv and params.conv_len) to the input.
The output of the convolutional layers is given to LSTM layers (as configured
with params.num_layers and params.num_nodes).
The final state of the all LSTM layers are concatenated and fed to a fully
connected layer to obtain the final classification scores.
Args:
features: dictionary with keys: inks, lengths.
labels: one hot encoded classes
mode: one of tf.estimator.ModeKeys.{TRAIN, INFER, EVAL}
params: a parameter dictionary with the following keys: num_layers,
num_nodes, batch_size, num_conv, conv_len, num_classes, learning_rate.
Returns:
ModelFnOps for Estimator API.
"""
def _get_input_tensors(features, labels):
"""Converts the input dict into inks, lengths, and labels tensors."""
# features[ink] is a sparse tensor that is [8, batch_maxlen, 3]
# inks will be a dense tensor of [8, maxlen, 3]
# shapes is [batchsize, 2]
shapes = features["shape"]
# lengths will be [batch_size]
lengths = tf.squeeze(
tf.slice(shapes, begin=[0, 0], size=[params.batch_size, 1]))
inks = tf.reshape(features["drawing"], [params.batch_size, -1, 3])
if labels is not None:
labels = tf.squeeze(labels)
return inks, lengths, labels
def _add_conv_layers(inks, lengths):
"""Adds convolution layers."""
convolved = inks
for i in range(len(params.num_conv)):
convolved_input = convolved
if params.batch_norm:
convolved_input = tf.layers.batch_normalization(
convolved_input,
training=(mode == tf.estimator.ModeKeys.TRAIN))
# Add dropout layer if enabled and not first convolution layer.
if i > 0 and params.dropout:
convolved_input = tf.layers.dropout(
convolved_input,
rate=params.dropout,
training=(mode == tf.estimator.ModeKeys.TRAIN))
convolved = tf.layers.conv1d(
convolved_input,
filters=params.num_conv[i],
kernel_size=params.conv_len[i],
activation=None,
strides=1,
padding="same",
name="conv1d_%d" % i)
return convolved, lengths
def _add_regular_rnn_layers(convolved, lengths):
"""Adds RNN layers."""
if params.cell_type == "lstm":
cell = tf.nn.rnn_cell.BasicLSTMCell
elif params.cell_type == "block_lstm":
cell = tf.contrib.rnn.LSTMBlockCell
cells_fw = [cell(params.num_nodes) for _ in range(params.num_layers)]
cells_bw = [cell(params.num_nodes) for _ in range(params.num_layers)]
if params.dropout > 0.0:
cells_fw = [tf.contrib.rnn.DropoutWrapper(cell) for cell in cells_fw]
cells_bw = [tf.contrib.rnn.DropoutWrapper(cell) for cell in cells_bw]
outputs, _, _ = tf.contrib.rnn.stack_bidirectional_dynamic_rnn(
cells_fw=cells_fw,
cells_bw=cells_bw,
inputs=convolved,
sequence_length=lengths,
dtype=tf.float32,
scope="rnn_classification")
return outputs
def _add_cudnn_rnn_layers(convolved):
"""Adds CUDNN LSTM layers."""
# Convolutions output [B, L, Ch], while CudnnLSTM is time-major.
convolved = tf.transpose(convolved, [1, 0, 2])
lstm = tf.contrib.cudnn_rnn.CudnnLSTM(
num_layers=params.num_layers,
num_units=params.num_nodes,
dropout=params.dropout if mode == tf.estimator.ModeKeys.TRAIN else 0.0,
direction="bidirectional")
outputs, _ = lstm(convolved)
# Convert back from time-major outputs to batch-major outputs.
outputs = tf.transpose(outputs, [1, 0, 2])
return outputs
def _add_rnn_layers(convolved, lengths):
"""Adds recurrent neural network layers depending on the cell type."""
if params.cell_type != "cudnn_lstm":
outputs = _add_regular_rnn_layers(convolved, lengths)
else:
outputs = _add_cudnn_rnn_layers(convolved)
# outputs is [batch_size, L, N] where L is the maximal sequence length and N
# the number of nodes in the last layer.
mask = tf.tile(
tf.expand_dims(tf.sequence_mask(lengths, tf.shape(outputs)[1]), 2),
[1, 1, tf.shape(outputs)[2]])
zero_outside = tf.where(mask, outputs, tf.zeros_like(outputs))
outputs = tf.reduce_sum(zero_outside, axis=1)
return outputs
def _add_fc_layers(final_state):
"""Adds a fully connected layer."""
return tf.layers.dense(final_state, params.num_classes)
# Build the model.
inks, lengths, labels = _get_input_tensors(features, labels)
convolved, lengths = _add_conv_layers(inks, lengths)
final_state = _add_rnn_layers(convolved, lengths)
logits = _add_fc_layers(final_state)
# Add the loss.
cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits))
# Add the optimizer.
train_op = tf.contrib.layers.optimize_loss(
loss=cross_entropy,
global_step=tf.train.get_global_step(),
learning_rate=params.learning_rate,
optimizer="Adam",
# some gradient clipping stabilizes training in the beginning.
clip_gradients=params.gradient_clipping_norm,
summaries=["learning_rate", "loss", "gradients", "gradient_norm"])
# Compute current predictions.
predictions = tf.argmax(logits, axis=1)
return tf.estimator.EstimatorSpec(
mode=mode,
predictions={"logits": logits, "predictions": predictions},
loss=cross_entropy,
train_op=train_op,
eval_metric_ops={"accuracy": tf.metrics.accuracy(labels, predictions)})
def create_estimator_and_specs(run_config):
"""Creates an Experiment configuration based on the estimator and input fn."""
model_params = tf.contrib.training.HParams(
num_layers=FLAGS.num_layers,
num_nodes=FLAGS.num_nodes,
batch_size=FLAGS.batch_size,
num_conv=ast.literal_eval(FLAGS.num_conv),
conv_len=ast.literal_eval(FLAGS.conv_len),
num_classes=get_num_classes(),
learning_rate=FLAGS.learning_rate,
gradient_clipping_norm=FLAGS.gradient_clipping_norm,
cell_type=FLAGS.cell_type,
batch_norm=FLAGS.batch_norm,
dropout=FLAGS.dropout)
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config,
params=model_params)
train_spec = tf.estimator.TrainSpec(input_fn=get_input_fn(
mode=tf.estimator.ModeKeys.TRAIN,
tfrecord_pattern=FLAGS.training_data,
batch_size=FLAGS.batch_size), max_steps=FLAGS.steps)
eval_spec = tf.estimator.EvalSpec(input_fn=get_input_fn(
mode=tf.estimator.ModeKeys.EVAL,
tfrecord_pattern=FLAGS.eval_data,
batch_size=FLAGS.batch_size))
return estimator, train_spec, eval_spec
def main(unused_args):
estimator, train_spec, eval_spec = create_estimator_and_specs(
run_config=tf.estimator.RunConfig(
model_dir=FLAGS.model_dir,
save_checkpoints_secs=300,
save_summary_steps=100))
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.register("type", "bool", lambda v: v.lower() == "true")
parser.add_argument(
"--training_data",
type=str,
default="/Train",
help="Path to training data (tf.Example in TFRecord format)")
parser.add_argument(
"--eval_data",
type=str,
default="/Eval",
help="Path to evaluation data (tf.Example in TFRecord format)")
parser.add_argument(
"--classes_file",
type=str,
default="",
help="Path to a file with the classes - one class per line")
parser.add_argument(
"--num_layers",
type=int,
default=3,
help="Number of recurrent neural network layers.")
parser.add_argument(
"--num_nodes",
type=int,
default=128,
help="Number of node per recurrent network layer.")
parser.add_argument(
"--num_conv",
type=str,
default="[48, 64, 96]",
help="Number of conv layers along with number of filters per layer.")
parser.add_argument(
"--conv_len",
type=str,
default="[5, 5, 3]",
help="Length of the convolution filters.")
parser.add_argument(
"--cell_type",
type=str,
default="lstm",
help="Cell type used for rnn layers: cudnn_lstm, lstm or block_lstm.")
parser.add_argument(
"--batch_norm",
type="bool",
default="False",
help="Whether to enable batch normalization or not.")
parser.add_argument(
"--learning_rate",
type=float,
default=0.0001,
help="Learning rate used for training.")
parser.add_argument(
"--gradient_clipping_norm",
type=float,
default=9.0,
help="Gradient clipping norm used during training.")
parser.add_argument(
"--dropout",
type=float,
default=0.3,
help="Dropout used for convolutions and bidi lstm layers.")
parser.add_argument(
"--steps",
type=int,
default=100000,
help="Number of training steps.")
parser.add_argument(
"--batch_size",
type=int,
default=8,
help="Batch size to use for training/evaluation.")
parser.add_argument(
"--model_dir",
type=str,
default="A:\Code\Machine Learning\Software Engineering project\Quick Draw\Model Checkpoints",
help="Path for storing the model checkpoints.")
parser.add_argument(
"--self_test",
type="bool",
default="False",
help="Whether to enable batch normalization or not.")
parser.add_argument(
"--classes_path",
type=str,
default="A:\Code\Machine Learning\Software Engineering project\Quick Draw\quickdraw-dataset-master\categories.txt",
help="Path of the text file which contains name of classes"
)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
我有TFRecords格式的数据。在运行代码之后，它将创建一个评估文件夹，其中包含events.out.my个系统名称文件，events.out文件夹中的更多main个文件，graph.pbtxt文件，{{1} }，model.ckpt-0.data-0000-of-0001，model.ckpt.index和名为model.ckpt.meta的文件。这些文件是什么？如何将它们用于预测测试数据？
此外，当数据由9个训练和9个评估文件组成时，为什么只有一个checkpoint。
另一个问题：当我运行代码时，它很快完成了执行，而大多数深度学习模型都需要很长时间才能执行。我的意思是说，有3450000个培训示例和345000个评估示例，所有操作都在一分钟内完成。我是TensorFlow的新手，所以请记住这一点。我对此了解不多。
更新：将行model.ckpt.data添加到代码中后，我得到以下输出：
tf.logging.set_verbosity('INFO')为什么精度为0.0，损耗为0.0。我认为这有一些问题，但不完全清楚。我认为并非所有示例都在进行培训。
答案 0 :(得分：0)
最明显的原因，您的模型没有收到任何训练数据。据我了解，您的get_input_fn包含不必要的功能，可能无法按您的要求运行。
特别是，使用if mode==train twice
if mode == tf.estimator.ModeKeys.TRAIN:
dataset = dataset.shuffle(buffer_size=10)
dataset = dataset.repeat()
# Preprocesses 10 files concurrently and interleaves records from each file.
dataset = dataset.interleave(
tf.data.TFRecordDataset,
cycle_length=10,
block_length=1)
dataset = dataset.map(
functools.partial(_parse_tfexample_fn, mode=mode),
num_parallel_calls=10)
dataset = dataset.prefetch(10000)
if mode == tf.estimator.ModeKeys.TRAIN:
dataset = dataset.shuffle(buffer_size=1000000)
返回迭代器，该迭代器不应与Estimator API一起使用
features, labels = dataset.make_one_shot_iterator().get_next()
return features, labels
为了进行调试，请先尝试使用简单的输入管道并逐步添加功能
dataset = tf.data.TFRecordDataset.list_files(tfrecord_pattern)
dataset = dataset.shuffle(buffer_size=10)
dataset = dataset.repeat()
dataset = dataset.interleave(tf.data.TFRecordDataset, cycle_length=10, block_length=1)
dataset = dataset.map(_parse_tfexample_fn, num_parallel_calls=10)
dataset = dataset.prefetch(1)
dataset = dataset.padded_batch(batch_size, padded_shapes=dataset.output_shapes)
return dataset
首先尝试使用estimator.train
P.s.这不是立即可用的功能，您应该根据输入数据对其进行调整。