Lihat di TensorFlow.org
Jalankan di Google Colab
Lihat sumber di GitHubPanduan ini melatih model jaringan saraf untuk mengklasifikasikan gambar pakaian, seperti sepatu kets dan kemeja , menyimpan model terlatih, lalu menyajikannya dengan Penyajian TensorFlow . Fokusnya adalah pada Penyajian TensorFlow, bukan pemodelan dan pelatihan di TensorFlow, jadi untuk contoh lengkap yang berfokus pada pemodelan dan pelatihan, lihat contoh Klasifikasi Dasar .
Panduan ini menggunakan tf.keras , API level tinggi untuk membuat dan melatih model di 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__))
Installing dependencies for Colab environment [K |████████████████████████████████| 2.4MB 4.6MB/s [?25hInstalling TensorFlow TensorFlow 2.x selected. TensorFlow version: 2.1.0-rc1
Buat model Anda
Impor set data Fashion MNIST
Panduan ini menggunakan dataset Fashion MNIST yang berisi 70.000 gambar grayscale dalam 10 kategori. Gambar menunjukkan setiap artikel pakaian dengan resolusi rendah (28 kali 28 piksel), seperti yang terlihat di sini:
 |
| Gambar 1. Sampel Fashion-MNIST (oleh Zalando, MIT Licence). |
Fashion MNIST dimaksudkan sebagai pengganti drop-in untuk kumpulan data MNIST klasik — sering digunakan sebagai "Halo, Dunia" dari program pembelajaran mesin untuk computer vision. Anda dapat mengakses Fashion MNIST langsung dari TensorFlow, cukup impor dan muat datanya.
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
Latih dan evaluasi model Anda
Mari gunakan CNN yang sesederhana mungkin, karena kita tidak fokus pada bagian pemodelan.
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
Simpan model Anda
Untuk memuat model terlatih kita ke dalam Penyajian TensorFlow, pertama-tama kita harus menyimpannya dalam format SavedModel . Ini akan membuat file protobuf dalam hierarki direktori yang terdefinisi dengan baik, dan akan menyertakan nomor versi. TensorFlow Serving memungkinkan kita memilih versi model mana, atau "servable" yang ingin kita gunakan saat membuat permintaan inferensi. Setiap versi akan diekspor ke sub-direktori yang berbeda di bawah jalur yang diberikan.
# 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
Periksa model simpanan Anda
Kami akan menggunakan utilitas baris perintah saved_model_cli untuk melihat MetaGraphDefs (model) dan SignatureDefs (metode yang dapat Anda panggil) di SavedModel kami. Lihat diskusi CLI SavedModel ini di Panduan 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
Itu memberi tahu kita banyak hal tentang model kita! Dalam hal ini kami hanya melatih model kami, jadi kami sudah mengetahui input dan outputnya, tetapi jika tidak, ini akan menjadi informasi penting. Itu tidak memberi tahu kita segalanya, seperti fakta bahwa ini adalah data gambar skala abu-abu misalnya, tetapi ini adalah awal yang bagus.
Sajikan model Anda dengan Penyajian TensorFlow
Tambahkan URI distribusi TensorFlow Serving sebagai sumber paket:
Kami sedang mempersiapkan untuk menginstal TensorFlow Serving menggunakan Aptitude karena Colab ini berjalan di lingkungan Debian. Kami akan menambahkan paket tensorflow-model-server ke daftar paket yang diketahui Aptitude. Perhatikan bahwa kami menjalankan sebagai 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]
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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]
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Get:11 http://storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server amd64 Packages [354 B]
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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]
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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.
Instal Penyajian TensorFlow
Ini semua yang Anda butuhkan - satu baris perintah!
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) ...
Mulai jalankan Penyajian TensorFlow
Di sinilah kami mulai menjalankan Penyajian TensorFlow dan memuat model kami. Setelah dimuat, kita dapat mulai membuat permintaan inferensi menggunakan REST. Ada beberapa parameter penting:
-
rest_api_port: Port yang akan Anda gunakan untuk permintaan REST. -
model_name: Anda akan menggunakan ini di URL permintaan REST. Bisa apa saja. -
model_base_path: Ini adalah jalur ke direktori tempat Anda menyimpan model Anda.
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 ...
Buat permintaan ke model Anda di TensorFlow Serving
Pertama, mari kita lihat contoh acak dari data pengujian kita.
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]]))
Oke, kelihatannya menarik. Seberapa sulit bagi Anda untuk mengenali? Sekarang mari buat objek JSON untuk kumpulan tiga permintaan inferensi, dan lihat seberapa baik model kita mengenali berbagai hal:
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]]]]}
Buat permintaan REST
Versi terbaru dari servable
Kami akan mengirimkan permintaan prediksi sebagai POST ke titik akhir REST server kami, dan memberikan tiga contoh. Kami akan meminta server kami untuk memberi kami versi terbaru dari server kami dengan tidak menentukan versi tertentu.
!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]))
Versi tertentu dari servable
Sekarang mari tentukan versi tertentu dari servable kita. Karena kita hanya punya satu, pilih versi 1. Kita juga akan melihat ketiga hasil.
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]))
