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#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# https://www.apache.org/licenses/LICENSE-2.0
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This is an introductory TensorFlow tutorial shows how to:
tf.data.Dataset
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from __future__ import absolute_import, division, print_function
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
except Exception:
pass
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import tensorflow as tf
A Tensor is a multi-dimensional array. Similar to NumPy ndarray objects, tf.Tensor objects have a data type and a shape. Additionally, tf.Tensors can reside in accelerator memory (like a GPU). TensorFlow offers a rich library of operations (tf.add, tf.matmul, tf.linalg.inv etc.) that consume and produce tf.Tensors. These operations automatically convert native Python types, for example:
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print(tf.add(1, 2))
print(tf.add([1, 2], [3, 4]))
print(tf.square(5))
print(tf.reduce_sum([1, 2, 3]))
# Operator overloading is also supported
print(tf.square(2) + tf.square(3))
Each tf.Tensor has a shape and a datatype:
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x = tf.matmul([[1]], [[2, 3]])
print(x)
print(x.shape)
print(x.dtype)
The most obvious differences between NumPy arrays and tf.Tensors are:
Converting between a TensorFlow tf.Tensors and a NumPy ndarray is easy:
Tensors are explicitly converted to NumPy ndarrays using their .numpy() method. These conversions are typically cheap since the array and tf.Tensor share the underlying memory representation, if possible. However, sharing the underlying representation isn't always possible since the tf.Tensor may be hosted in GPU memory while NumPy arrays are always backed by host memory, and the conversion involves a copy from GPU to host memory.
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import numpy as np
ndarray = np.ones([3, 3])
print("TensorFlow operations convert numpy arrays to Tensors automatically")
tensor = tf.multiply(ndarray, 42)
print(tensor)
print("And NumPy operations convert Tensors to numpy arrays automatically")
print(np.add(tensor, 1))
print("The .numpy() method explicitly converts a Tensor to a numpy array")
print(tensor.numpy())
Many TensorFlow operations are accelerated using the GPU for computation. Without any annotations, TensorFlow automatically decides whether to use the GPU or CPU for an operation—copying the tensor between CPU and GPU memory, if necessary. Tensors produced by an operation are typically backed by the memory of the device on which the operation executed, for example:
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x = tf.random.uniform([3, 3])
print("Is there a GPU available: "),
print(tf.config.experimental.list_physical_devices("GPU"))
print("Is the Tensor on GPU #0: "),
print(x.device.endswith('GPU:0'))
The Tensor.device property provides a fully qualified string name of the device hosting the contents of the tensor. This name encodes many details, such as an identifier of the network address of the host on which this program is executing and the device within that host. This is required for distributed execution of a TensorFlow program. The string ends with GPU:<N> if the tensor is placed on the N-th GPU on the host.
In TensorFlow, placement refers to how individual operations are assigned (placed on) a device for execution. As mentioned, when there is no explicit guidance provided, TensorFlow automatically decides which device to execute an operation and copies tensors to that device, if needed. However, TensorFlow operations can be explicitly placed on specific devices using the tf.device context manager, for example:
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import time
def time_matmul(x):
start = time.time()
for loop in range(10):
tf.matmul(x, x)
result = time.time()-start
print("10 loops: {:0.2f}ms".format(1000*result))
# Force execution on CPU
print("On CPU:")
with tf.device("CPU:0"):
x = tf.random.uniform([1000, 1000])
assert x.device.endswith("CPU:0")
time_matmul(x)
# Force execution on GPU #0 if available
if tf.config.experimental.list_physical_devices("GPU"):
print("On GPU:")
with tf.device("GPU:0"): # Or GPU:1 for the 2nd GPU, GPU:2 for the 3rd etc.
x = tf.random.uniform([1000, 1000])
assert x.device.endswith("GPU:0")
time_matmul(x)
This section uses the tf.data.Dataset API to build a pipeline for feeding data to your model. The tf.data.Dataset API is used to build performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops.
DatasetCreate a source dataset using one of the factory functions like Dataset.from_tensors, Dataset.from_tensor_slices, or using objects that read from files like TextLineDataset or TFRecordDataset. See the TensorFlow Dataset guide for more information.
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ds_tensors = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6])
# Create a CSV file
import tempfile
_, filename = tempfile.mkstemp()
with open(filename, 'w') as f:
f.write("""Line 1
Line 2
Line 3
""")
ds_file = tf.data.TextLineDataset(filename)
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ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2)
ds_file = ds_file.batch(2)
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print('Elements of ds_tensors:')
for x in ds_tensors:
print(x)
print('\nElements in ds_file:')
for x in ds_file:
print(x)