MLflowの使い方紹介

import mlflow
mlflow.autolog()

"""Trains and evaluate a simple MLP
on the Reuters newswire topic classification task.
"""
import numpy as np
import keras
from keras.datasets import reuters
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.preprocessing.text import Tokenizer

# MLflowの設定
import mlflow
mlflow.set_tracking_uri("https://xxxx.xx/") #トラッキングサーバーの設定
mlflow.tensorflow.autolog(every_n_iter=1) # eveny_n_iterで何epoch毎に記録を取るか設定。デフォルトは100の為、数epochだと学習曲線が書けない。

# パラメータ
max_words = 1000
batch_size = 64
epochs = 20

# データ読み込み
print("Loading data...")
(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_words, test_split=0.2)

print(len(x_train), "train sequences")
print(len(x_test), "test sequences")

num_classes = np.max(y_train) + 1
print(num_classes, "classes")

print("Vectorizing sequence data...")
tokenizer = Tokenizer(num_words=max_words)
x_train = tokenizer.sequences_to_matrix(x_train, mode="binary")
x_test = tokenizer.sequences_to_matrix(x_test, mode="binary")
print("x_train shape:", x_train.shape)
print("x_test shape:", x_test.shape)

print("Convert class vector to binary class matrix " "(for use with categorical_crossentropy)")
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
print("y_train shape:", y_train.shape)
print("y_test shape:", y_test.shape)

#モデル作成
print("Building model...")
model = Sequential()
model.add(Dense(512, input_shape=(max_words,)))
model.add(Activation("relu"))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation("softmax"))

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

#学習
history = model.fit(
    x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_split=0.1
)
score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)
print("Test score:", score[0])
print("Test accuracy:", score[1])