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__author__ = 'Guillaume Genthial'
__date__ = '2018-09-22'
See the README.md for an overview of this notebook and its goals.
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from distutils.version import LooseVersion
import sys
if LooseVersion(sys.version) < LooseVersion('3.4'):
raise Exception('You need python>=3.4, but you have {}'.format(sys.version))
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# Standard
from pathlib import Path
# External
import numpy as np
import tensorflow as tf
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if LooseVersion(tf.__version__) < LooseVersion('1.9'):
raise Exception('You need tensorflow>=1.9, but you have {}'.format(tf.__version__))
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# You need to activate it at program startup
tf.enable_eager_execution()
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X = tf.random_normal([2, 4])
h = tf.layers.dense(X, 2, activation=tf.nn.relu)
y = tf.nn.softmax(h)
print(y)
print(y.numpy())
Here, X, h, y are nodes of the computational graph. But you can actually get the value of these nodes!
In the past you would have written
X = tf.placeholder(dtype=tf.float32, shape=[2, 4])
h = tf.layers.dense(X, 2, activation=tf.nn.relu)
y = tf.nn.softmax(h)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(y, feed_dict={X: np.random.normal(size=[2, 4])})
tf.data: feeding data into the graphtf.placeholders is replaced by tf.data.Dataset.
x = tf.placeholder(dtype=tf.int32, shape=[None, 5])
with tf.Session() as sess:
x_eval = sess.run(x, feed_dict={x: x_np})
print(x_eval)
np.arrayBelow is a simple example where we have a np.array, one row = one example.
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x_np = np.array([[i]*5 for i in range(10)])
x_np
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We create a Dataset from this array.
This dataset is a node of the graph.
Each time you query its value, it will move to the next row of the underlying np.array.
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dataset = tf.data.Dataset.from_tensor_slices(x_np)
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for el in dataset:
print(el)
el is the equivalent of the former tf.placeholder. It's a node of the graph, to which you can apply any Tensorflow operations.
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path = 'test.txt'
with Path(path).open() as f:
print(f.read())
The following does just the same as above, but now el is a tf.Tensor of dtype=tf.string!
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dataset = tf.data.TextLineDataset([path])
for el in dataset:
print(el)
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def generator_fn():
for _ in range(2):
yield b'Hello world'
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dataset = (tf.data.Dataset.from_generator(
generator_fn,
output_types=(tf.string), # Define type and shape of your generator_fn output
output_shapes=())) # like you would have for your `placeholders`
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for el in dataset:
print(el)
Note: the buffer_size is the number of elements you load in the RAM before starting to sample from it. If it's too small (1 is no shuffling at all), it won't be efficient. Ideally, your buffer_size is the same as the number of elements in your dataset. But because not all datasets fit in RAM, you need to be able to set it manually.
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dataset = dataset.shuffle(buffer_size=10)
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for el in dataset:
print(el)
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dataset = dataset.repeat(2) # 2 epochs
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for el in dataset:
print(el)
Note: while map is super handy when working with images (TensorFlow has a lot of image preprocessing functions and efficiency is crucial), it's not as practical for NLP, because you're now working with tensors. We found it easier to write the most of the preprocessing logic in python, in a generator_fn, before feeding it to the graph.
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dataset = dataset.map(
lambda t: tf.string_split([t], delimiter=' ').values,
num_parallel_calls=4) # Multithreading
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for el in dataset:
print(el)
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dataset = dataset.batch(batch_size=3)
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for el in dataset:
print(el)
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def generator_fn():
yield [1, 2]
yield [1, 2, 3]
dataset = tf.data.Dataset.from_generator(
generator_fn,
output_types=(tf.int32),
output_shapes=([None]))
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dataset = dataset.padded_batch(
batch_size=2,
padded_shapes=([None]),
padding_values=(4)) # Optional, if not set will default to 0
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for el in dataset:
print(el)
Notice that a 4 has been appended at the end of the first row.
And much more: prefetch, zip, concatenate, skip, take etc.
See the documentation.
Note: the recommended standard workflow is
shufflerepeat (repeat after shuffle so that one epoch = all the examples)map, using the num_parallel_calls argument to get multithreading for free.batch or padded_batchprefetch (will prefetch data on the GPU so that it doesn't suffer from any data starvation – and only use 80% of your expensive GPU).
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def tf_tokenize(t):
return tf.string_split([t], delimiter=' ').values
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dataset = tf.data.TextLineDataset(['test.txt'])
dataset = dataset.map(tf_tokenize)
for el in dataset:
print(el)
You're probably used to performing the lookup token -> token_idx outside TensorFlow. However, tf.contrib.lookup provides exactly this functionality. It's fast, and when exporting the model for serving, it will consider your vocab.txt file as a model's resource and keep it with the model!
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# One lexeme per line
path_vocab = 'vocab.txt'
with Path(path_vocab).open() as f:
for idx, line in enumerate(f):
print(idx, ' -> ', line.strip())
To use it in TensorFlow:
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# The last idx (2) will be used for unknown words
lookup_table = tf.contrib.lookup.index_table_from_file(
path_vocab, num_oov_buckets=1)
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for el in dataset:
print(lookup_table.lookup(el))
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# We tokenize by white space and assign these ids
tok_to_idx = {'hello': 0, 'world': 1, '<odd>': 2, '<even>': 3}
def tokens_generator():
with Path(path).open() as f:
for line in f:
# Tokenize by white space
tokens = line.strip().split()
token_ids = []
for tok in tokens:
# Look for digits
if tok.isdigit():
if int(tok) % 2 == 0:
tok = '<even>'
else:
tok = '<odd>'
token_ids.append(tok_to_idx.get(tok.lower(), len(tok_to_idx)))
yield (token_ids, len(token_ids))
def get_label(token_id):
if token_id == 2:
return 1
elif token_id == 3:
return 2
else:
return 0
def labels_generator():
for token_ids, _ in tokens_generator():
yield [get_label(tok_id) for tok_id in token_ids]
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dataset = tf.data.Dataset.from_generator(
tokens_generator,
output_types=(tf.int32, tf.int32),
output_shapes=([None], ()))
for el in dataset:
print(el)
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batch_size = 4
vocab_size = 4
dim = 100
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shapes = ([None], ())
defaults = (0, 0)
# The last sentence is longer: need padding
dataset = dataset.padded_batch(
batch_size, shapes, defaults)
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# Define all variables (In eager execution mode, have to be done just once)
# Otherwise you would create new variable at each loop iteration!
embeddings = tf.get_variable('embeddings', shape=[vocab_size, dim])
lstm_cell = tf.contrib.rnn.LSTMCell(100)
dense_layer = tf.layers.Dense(3, activation=tf.nn.relu)
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for tokens, sequence_length in dataset:
token_embeddings = tf.nn.embedding_lookup(embeddings, tokens)
lstm_output, _ = tf.nn.dynamic_rnn(
lstm_cell, token_embeddings, dtype=tf.float32, sequence_length=sequence_length)
logits = dense_layer(lstm_output)
print(logits.shape)
No error/cryptic messages about some shape mismatch – seems like our TensorFlow logic is fine.
tf.estimator)tf.estimator uses the traditional graph-based environment (no eager execution).
If you use the tf.estimator interface, you will get for free :
People used to write custom model classes
class Model:
def get_feed_dict(self, X, y):
return {self.X: X, self.y: y}
def build(self):
do_stuff()
def train(self, X, y):
with tf.Session() as sess:
do_some_training()
tf.estimatorNow there is a common interface for models.
def input_fn():
# Return a tf.data.Dataset that yields a tuple features, labels
return dataset
def model_fn(features, labels, mode, params):
"""
Parameters
----------
features: tf.Tensor or nested structure
Returned by `input_fn`
labels: tf.Tensor of nested structure
Returned by `input_fn`
mode: tf.estimator.ModeKeys
Either PREDICT / EVAL / TRAIN
params: dict
Hyperparams
Returns
-------
tf.estimator.EstimatorSpec
"""
if mode == tf.estimator.ModeKeys.TRAIN:
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
elif mode == ...
...
estimator = tf.estimator.Estimator(
model_fn=model_fn, params=params)
estimator.train(input_fn)
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# Clear all the objects we defined above, to be sure
# we don't mess with anything
tf.reset_default_graph()
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def input_fn():
# Create datasets for features and labels
dataset_tokens = tf.data.Dataset.from_generator(
tokens_generator,
output_types=(tf.int32, tf.int32),
output_shapes=([None], ()))
dataset_output = tf.data.Dataset.from_generator(
labels_generator,
output_types=(tf.int32),
output_shapes=([None]))
# Zip features and labels in one Dataset
dataset = tf.data.Dataset.zip((dataset_tokens, dataset_output))
# Shuffle, repeat, batch and prefetch
shapes = (([None], ()), [None])
defaults = ((0, 0), 0)
dataset = (dataset
.shuffle(10)
.repeat(100)
.padded_batch(4, shapes, defaults)
.prefetch(1))
# Dataset yields tuple of features, labels
return dataset
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def model_fn(features, labels, mode, params):
# Args features and labels are the same as returned by the dataset
tokens, sequence_length = features
# For Serving (ignore this)
if isinstance(features, dict):
tokens = features['tokens']
sequence_length = features['sequence_length']
# 1. Define the graph
vocab_size = params['vocab_size']
dim = params['dim']
embeddings = tf.get_variable('embeddings', shape=[vocab_size, dim])
token_embeddings = tf.nn.embedding_lookup(embeddings, tokens)
lstm_cell = tf.contrib.rnn.LSTMCell(20)
lstm_output, _ = tf.nn.dynamic_rnn(
lstm_cell, token_embeddings, dtype=tf.float32)
logits = tf.layers.dense(lstm_output, 3)
preds = tf.argmax(logits, axis=-1)
# 2. Define EstimatorSpecs for PREDICT
if mode == tf.estimator.ModeKeys.PREDICT:
# Predictions is any nested object (dict is convenient)
predictions = {'logits': logits, 'preds': preds}
# export_outputs is for serving (ignore this)
export_outputs = {
'predictions': tf.estimator.export.PredictOutput(predictions)}
return tf.estimator.EstimatorSpec(mode, predictions=predictions,
export_outputs=export_outputs)
else:
# 3. Define loss and metrics
# Define weights to account for padding
weights = tf.sequence_mask(sequence_length)
loss = tf.losses.sparse_softmax_cross_entropy(
logits=logits, labels=labels, weights=weights)
metrics = {
'accuracy': tf.metrics.accuracy(labels=labels, predictions=preds),
}
# For Tensorboard
for k, v in metrics.items():
# v[1] is the update op of the metrics object
tf.summary.scalar(k, v[1])
# 4. Define EstimatorSpecs for EVAL
# Having an eval mode and metrics in Tensorflow allows you to use
# built-in early stopping (see later)
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode, loss=loss,
eval_metric_ops=metrics)
# 5. Define EstimatorSpecs for TRAIN
elif mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
train_op = (tf.train.AdamOptimizer(learning_rate=0.1)
.minimize(loss, global_step=global_step))
return tf.estimator.EstimatorSpec(mode, loss=loss,
train_op=train_op)
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params = {
'vocab_size': 4,
'dim': 3
}
estimator = tf.estimator.Estimator(
model_fn=model_fn,
model_dir='model', # Will save the weights here automatically
params=params)
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estimator.train(input_fn)
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Check evaluate, train_and_evaluate... documentation.
Example with early stopping, where we run evaluation every 2 minutes (120 seconds).
hook = tf.contrib.estimator.stop_if_no_increase_hook(
estimator, 'accuracy', 500, min_steps=8000, run_every_secs=120)
train_spec = tf.estimator.TrainSpec(input_fn=input_fn, hooks=[hook])
eval_spec = tf.estimator.EvalSpec(input_fn=input_fn, throttle_secs=120)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
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# Iterate over the 2 first elements of the (shuffled) dataset and yield predictions
# You need to write variants of your input_fn for eval / predict modes
for idx, predictions in enumerate(estimator.predict(input_fn)):
print(predictions['preds'])
if idx > 0:
break
Exporting an inference graph and the serving signature is "simple" (though the serving_fn interface could be improved). The cool thing is that once you have your tf.estimator and your serving_input_fn, you can just use tensorflow_serving and get a RESTful API serving your model!
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def serving_input_fn():
tokens = tf.placeholder(
dtype=tf.int32, shape=[None, None], name="tokens")
sequence_length = tf.size(tokens)
features = {'tokens': tokens, 'sequence_length': sequence_length}
return tf.estimator.export.ServingInputReceiver(
features=features, receiver_tensors=tokens)
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estimator.export_savedmodel('export', serving_input_fn)
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docker pull tensorflow/servingdocker run -p 8501:8501 \
--mount type=bind,\
source=path_to_your_export_model,\
target=/models/dummy \
-e MODEL_NAME=dummy -t tensorflow/serving &curl -d '{"instances": [[0, 1, 2],[0, 1, 3]]}' -X POST \
http://localhost:8501/v1/models/dummy:predict