Ver no TensorFlow.org
Executar no Google Colab
Ver fonte no GitHubEste guia treina um modelo de rede neural para classificar imagens de roupas, como tênis e camisas , salva o modelo treinado e o exibe com o TensorFlow Serving . O foco está no TensorFlow Serving, em vez de modelagem e treinamento no TensorFlow, portanto, para um exemplo completo com foco na modelagem e treinamento, consulte o exemplo de Classificação Básica .
Este guia usa tf.keras , uma API de alto nível para criar e treinar modelos no TensorFlow.
import sys
# Confirm that we're using Python 3
assert sys.version_info.major is 3, 'Oops, not running Python 3. Use Runtime > Change runtime type'
# TensorFlow and tf.keras
print("Installing dependencies for Colab environment")
!pip install -Uq grpcio==1.26.0
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import os
import subprocess
print('TensorFlow version: {}'.format(tf.__version__))
Crie o seu modelo
Importe o conjunto de dados Fashion MNIST
Este guia usa o conjunto de dados Fashion MNIST, que contém 70.000 imagens em tons de cinza em 10 categorias. As imagens mostram peças individuais de roupa em baixa resolução (28 por 28 pixels), como pode ser visto aqui:
 |
| Figura 1. Amostras do Fashion-MNIST (por Zalando, MIT License). |
O Fashion MNIST pretende ser um substituto imediato para o conjunto de dados MNIST clássico - frequentemente usado como o "Hello, World" de programas de aprendizado de máquina para visão computacional. Você pode acessar o Fashion MNIST diretamente do TensorFlow, basta importar e carregar os dados.
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
# scale the values to 0.0 to 1.0
train_images = train_images / 255.0
test_images = test_images / 255.0
# reshape for feeding into the model
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)
test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
print('\ntrain_images.shape: {}, of {}'.format(train_images.shape, train_images.dtype))
print('test_images.shape: {}, of {}'.format(test_images.shape, test_images.dtype))
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz 32768/29515 [=================================] - 0s 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz 26427392/26421880 [==============================] - 0s 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz 8192/5148 [===============================================] - 0s 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz 4423680/4422102 [==============================] - 0s 0us/step train_images.shape: (60000, 28, 28, 1), of float64 test_images.shape: (10000, 28, 28, 1), of float64
Treine e avalie seu modelo
Vamos usar o CNN mais simples possível, já que não estamos focados na parte de modelagem.
model = keras.Sequential([
keras.layers.Conv2D(input_shape=(28,28,1), filters=8, kernel_size=3,
strides=2, activation='relu', name='Conv1'),
keras.layers.Flatten(),
keras.layers.Dense(10, activation=tf.nn.softmax, name='Softmax')
])
model.summary()
testing = False
epochs = 5
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=epochs)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('\nTest accuracy: {}'.format(test_acc))
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= Conv1 (Conv2D) (None, 13, 13, 8) 80 _________________________________________________________________ flatten (Flatten) (None, 1352) 0 _________________________________________________________________ Softmax (Dense) (None, 10) 13530 ================================================================= Total params: 13,610 Trainable params: 13,610 Non-trainable params: 0 _________________________________________________________________ Train on 60000 samples Epoch 1/5 60000/60000 [==============================] - 11s 185us/sample - loss: 0.5466 - accuracy: 0.8087 Epoch 2/5 60000/60000 [==============================] - 5s 79us/sample - loss: 0.4032 - accuracy: 0.8580 Epoch 3/5 60000/60000 [==============================] - 5s 76us/sample - loss: 0.3613 - accuracy: 0.8712 Epoch 4/5 60000/60000 [==============================] - 5s 75us/sample - loss: 0.3406 - accuracy: 0.8797 Epoch 5/5 60000/60000 [==============================] - 4s 75us/sample - loss: 0.3247 - accuracy: 0.8848 10000/10000 [==============================] - 1s 73us/sample - loss: 0.3510 - accuracy: 0.8747 Test accuracy: 0.8747000098228455
Salve seu modelo
Para carregar nosso modelo treinado no TensorFlow Serving, primeiro precisamos salvá-lo no formato SavedModel . Isso criará um arquivo protobuf em uma hierarquia de diretórios bem definida e incluirá um número de versão. O TensorFlow Serving nos permite selecionar qual versão de um modelo ou "servível" queremos usar ao fazer solicitações de inferência. Cada versão será exportada para um subdiretório diferente no caminho fornecido.
# Fetch the Keras session and save the model
# The signature definition is defined by the input and output tensors,
# and stored with the default serving key
import tempfile
MODEL_DIR = tempfile.gettempdir()
version = 1
export_path = os.path.join(MODEL_DIR, str(version))
print('export_path = {}\n'.format(export_path))
tf.keras.models.save_model(
model,
export_path,
overwrite=True,
include_optimizer=True,
save_format=None,
signatures=None,
options=None
)
print('\nSaved model:')
!ls -l {export_path}
export_path = /tmp/1 WARNING:tensorflow:From /tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. Instructions for updating: If using Keras pass *_constraint arguments to layers. INFO:tensorflow:Assets written to: /tmp/1/assets Saved model: total 84 drwxr-xr-x 2 root root 4096 Jan 7 23:15 assets -rw-r--r-- 1 root root 74086 Jan 7 23:15 saved_model.pb drwxr-xr-x 2 root root 4096 Jan 7 23:15 variables
Examine seu modelo salvo
Usaremos o utilitário de linha de comando saved_model_cli para olhar os MetaGraphDefs (os modelos) e SignatureDefs (os métodos que você pode chamar) em nosso SavedModel. Veja esta discussão sobre a CLI SavedModel no Guia do TensorFlow.
saved_model_cli show --dir {export_path} --all
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['__saved_model_init_op']:
The given SavedModel SignatureDef contains the following input(s):
The given SavedModel SignatureDef contains the following output(s):
outputs['__saved_model_init_op'] tensor_info:
dtype: DT_INVALID
shape: unknown_rank
name: NoOp
Method name is:
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['Conv1_input'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 28, 28, 1)
name: serving_default_Conv1_input:0
The given SavedModel SignatureDef contains the following output(s):
outputs['Softmax'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 10)
name: StatefulPartitionedCall:0
Method name is: tensorflow/serving/predict
WARNING:tensorflow:From /tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Defined Functions:
Function Name: '__call__'
Option #1
Callable with:
Argument #1
Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
Argument #2
DType: bool
Value: True
Argument #3
DType: NoneType
Value: None
Option #2
Callable with:
Argument #1
inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
Argument #2
DType: bool
Value: True
Argument #3
DType: NoneType
Value: None
Option #3
Callable with:
Argument #1
inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
Argument #2
DType: bool
Value: False
Argument #3
DType: NoneType
Value: None
Option #4
Callable with:
Argument #1
Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
Argument #2
DType: bool
Value: False
Argument #3
DType: NoneType
Value: None
Function Name: '_default_save_signature'
Option #1
Callable with:
Argument #1
Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
Function Name: 'call_and_return_all_conditional_losses'
Option #1
Callable with:
Argument #1
inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
Argument #2
DType: bool
Value: True
Argument #3
DType: NoneType
Value: None
Option #2
Callable with:
Argument #1
inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
Argument #2
DType: bool
Value: False
Argument #3
DType: NoneType
Value: None
Option #3
Callable with:
Argument #1
Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
Argument #2
DType: bool
Value: False
Argument #3
DType: NoneType
Value: None
Option #4
Callable with:
Argument #1
Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
Argument #2
DType: bool
Value: True
Argument #3
DType: NoneType
Value: None
Isso nos diz muito sobre nosso modelo! Neste caso apenas treinamos nosso modelo, então já sabemos as entradas e saídas, mas se não o fizéssemos esta seria uma informação importante. Não nos diz tudo, como o fato de que se trata de dados de imagem em tons de cinza, por exemplo, mas é um ótimo começo.
Sirva seu modelo com o TensorFlow Serving
Adicione o URI de distribuição do TensorFlow Serving como uma fonte de pacote:
Estamos nos preparando para instalar o TensorFlow Serving usando o Aptitude, pois este Colab é executado em um ambiente Debian. Adicionaremos o tensorflow-model-server à lista de pacotes que o Aptitude conhece. Observe que estamos executando como root.
# This is the same as you would do from your command line, but without the [arch=amd64], and no sudo
# You would instead do:
# echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list && \
# curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add -
!echo "deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | tee /etc/apt/sources.list.d/tensorflow-serving.list && \
curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | apt-key add -
!apt update
deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 2943 100 2943 0 0 11496 0 --:--:-- --:--:-- --:--:-- 11496
OK
Get:1 http://storage.googleapis.com/tensorflow-serving-apt stable InRelease [3,012 B]
Get:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ InRelease [3,626 B]
Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease
Ign:4 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 InRelease
Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release
Get:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Release [564 B]
Get:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Release.gpg [833 B]
Hit:8 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease
Hit:9 http://archive.ubuntu.com/ubuntu bionic InRelease
Get:10 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]
Get:11 http://storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server amd64 Packages [354 B]
Get:12 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ Packages [81.6 kB]
Get:13 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]
Get:14 http://storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server-universal amd64 Packages [364 B]
Get:15 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic InRelease [15.4 kB]
Get:17 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Packages [30.4 kB]
Get:18 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]
Get:19 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic/main Sources [1,749 kB]
Get:20 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [796 kB]
Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [1,073 kB]
Get:22 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [776 kB]
Get:23 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [21.3 kB]
Get:24 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [10.8 kB]
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Get:26 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [35.5 kB]
Get:27 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic/main amd64 Packages [844 kB]
Fetched 7,019 kB in 4s (1,913 kB/s)
Reading package lists... Done
Building dependency tree
Reading state information... Done
21 packages can be upgraded. Run 'apt list --upgradable' to see them.
Instale o TensorFlow Serving
Isso é tudo que você precisa - uma linha de comando!
apt-get install tensorflow-model-server
Reading package lists... Done Building dependency tree Reading state information... Done The following package was automatically installed and is no longer required: libnvidia-common-430 Use 'apt autoremove' to remove it. The following NEW packages will be installed: tensorflow-model-server 0 upgraded, 1 newly installed, 0 to remove and 21 not upgraded. Need to get 140 MB of archives. After this operation, 0 B of additional disk space will be used. Get:1 http://storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server amd64 tensorflow-model-server all 2.0.0 [140 MB] Fetched 140 MB in 2s (78.8 MB/s) Selecting previously unselected package tensorflow-model-server. (Reading database ... 145674 files and directories currently installed.) Preparing to unpack .../tensorflow-model-server_2.0.0_all.deb ... Unpacking tensorflow-model-server (2.0.0) ... Setting up tensorflow-model-server (2.0.0) ...
Comece a executar o TensorFlow Serving
É aqui que começamos a executar o TensorFlow Serving e carregamos nosso modelo. Depois de carregar, podemos começar a fazer solicitações de inferência usando REST. Existem alguns parâmetros importantes:
-
rest_api_port: a porta que você usará para solicitações REST. -
model_name: você usará isso na URL de solicitações REST. Pode ser qualquer coisa. -
model_base_path: este é o caminho para o diretório onde você salvou seu modelo.
os.environ["MODEL_DIR"] = MODEL_DIR
nohup tensorflow_model_server \--rest_api_port=8501 \--model_name=fashion_model \--model_base_path="${MODEL_DIR}" >server.log 2>&1
Starting job # 0 in a separate thread.
tail server.log
[warn] getaddrinfo: address family for nodename not supported [evhttp_server.cc : 238] NET_LOG: Entering the event loop ...
Faça uma solicitação ao seu modelo no TensorFlow Serving
Primeiro, vamos dar uma olhada em um exemplo aleatório de nossos dados de teste.
def show(idx, title):
plt.figure()
plt.imshow(test_images[idx].reshape(28,28))
plt.axis('off')
plt.title('\n\n{}'.format(title), fontdict={'size': 16})
import random
rando = random.randint(0,len(test_images)-1)
show(rando, 'An Example Image: {}'.format(class_names[test_labels[rando]]))
Ok, isso parece interessante. É difícil para você reconhecer isso? Agora vamos criar o objeto JSON para um lote de três solicitações de inferência e ver como nosso modelo reconhece as coisas:
import json
data = json.dumps({"signature_name": "serving_default", "instances": test_images[0:3].tolist()})
print('Data: {} ... {}'.format(data[:50], data[len(data)-52:]))
Data: {"signature_name": "serving_default", "instances": ... [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]]]}
Faça solicitações REST
Versão mais recente do servable
Enviaremos uma solicitação de previsão como POST para o endpoint REST de nosso servidor e passaremos três exemplos. Solicitaremos ao nosso servidor que nos forneça a versão mais recente do nosso serviço, não especificando uma versão em particular.
!pip install -q requests
import requests
headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/fashion_model:predict', data=data, headers=headers)
predictions = json.loads(json_response.text)['predictions']
show(0, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(
class_names[np.argmax(predictions[0])], np.argmax(predictions[0]), class_names[test_labels[0]], test_labels[0]))
Uma versão particular do servable
Agora vamos especificar uma versão particular do nosso servable. Como temos apenas um, vamos selecionar a versão 1. Também examinaremos os três resultados.
headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/fashion_model/versions/1:predict', data=data, headers=headers)
predictions = json.loads(json_response.text)['predictions']
for i in range(0,3):
show(i, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(
class_names[np.argmax(predictions[i])], np.argmax(predictions[i]), class_names[test_labels[i]], test_labels[i]))
