순차 모델

1 개요[ | ]

sequential model
sequential 모델, 순차 모델

2 mean_squared_error[ | ]

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

np.random.seed(3)
tf.random.set_seed(3)

arr = np.loadtxt("https://raw.githubusercontent.com/gilbutITbook/006958/master/deeplearning/dataset/ThoraricSurgery.csv",delimiter=",")
X = arr[:,0:17]
Y = arr[:,17]

model = keras.Sequential([
                          layers.Dense(30, activation='relu', input_dim=17),
                          layers.Dense(1, activation='sigmoid'),
])
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
model.fit(X, Y, epochs=1000, batch_size=10)

print(model.evaluate(X, Y))

3 binary_crossentropy[ | ]

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

np.random.seed(3)
tf.random.set_seed(3)

arr = np.loadtxt("https://raw.githubusercontent.com/gilbutITbook/006958/master/deeplearning/dataset/ThoraricSurgery.csv",delimiter=",")
X = arr[:,0:17]
Y = arr[:,17]

model = keras.Sequential([
                          layers.Dense(30, activation='relu', input_dim=17),
                          layers.Dense(1, activation='sigmoid'),
])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, Y, epochs=1000, batch_size=10)

print(model.evaluate(X, Y))

4 참고[ | ]