[Английский] Обзор TensorFlow

машинное обучение искусственный интеллект TensorFlow

Key concept of Machine Learning and TensorFlow

TensorFlow

www.tensorflow.org
is an open source machine learning framework. Support language Python, C++, JavaScript, Java, Go, Swift.

Keras

is a high-level API to build and train models in TensorFlow

Colaboratory

col AB.research.Google.com/notebooks/i…
is a Google research project created to help disseminate machine learning education and research. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud.

Feature and lable

Briefly, feature is input; label is output. A feature is one column of the data in your input set. For instance, if you're trying to predict the type of pet someone will choose, your input features might include age, home region, family income, etc. The label is the final choice, such as dog, fish, iguana, rock, etc.
Once you've trained your model, you will give it sets of new input containing those features; it will return the predicted "label" (pet type) for that person. The features of the data, such as a house's size, age, etc.

Classification

Classify things such as classify images of clothing.

Regression

Predict the output of a continuous value according to the input, such as pridict house price according to the features of the house.

Key params for Karas to create a model

  • Layer
  • Функция потерь — измеряет, насколько точна модель во время обучения.
  • Оптимизатор — это то, как модель обновляется на основе данных, которые она видит, и ее функции потери.
  • Метрики — используются для мониторинга этапов обучения и тестирования.

Machine Learning Steps

  1. Import dataset.
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
  1. Build model(choose layers, loss function, optimizer, metrics).
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.train.AdamOptimizer(), 
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
  1. Train the model with train dataset, evaluate the trained model with the validate dataset.If not good enough, return to step2.
model.fit(train_images, train_labels, epochs=5)
test_loss, test_acc = model.evaluate(test_images, test_labels)
  1. Use the trained model to classify or predict new input data.
predictions = model.predict(test_images)

Overfitting and underfitting

image.png

A better understanding of Neural Network

playground.tensorflow.org/

image.png

TensorFlow demos

Classification: clissify clothing

import package

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

import dateset

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

create model

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.train.AdamOptimizer(), 
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

train model

model.fit(train_images, train_labels, epochs=5)

evaluate accuracy

test_loss, test_acc = model.evaluate(test_images, test_labels)

make predict

predictions = model.predict(test_images)

Regression: Predict house price

...
create model

model = keras.Sequential([
keras.layers.Dense(64, activation=tf.nn.relu,
                   input_shape=(train_data.shape[1],)),
keras.layers.Dense(64, activation=tf.nn.relu),
keras.layers.Dense(1)
])
optimizer = tf.train.RMSPropOptimizer(0.001)
model.compile(loss='mse',
            optimizer=optimizer,
            metrics=['mae'])

train model

history = model.fit(train_data, train_labels, epochs=EPOCHS,
                    validation_split=0.2, verbose=0,
                    callbacks=[PrintDot()])
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