import tensorflow as tf
from tensorflow_core import keras
from tensorflow.keras.models import Model
import numpy as np
import pandas as pd
import os
readings = tf.keras.Input(shape=(7, ))
x = keras.layers.Dense(8, activation="linear", kernel_initializer="glorot_uniform")(readings)
x = keras.layers.Dense(8, activation="relu", kernel_initializer="glorot_uniform")(x)
x = keras.layers.Dense(8, activation="relu", kernel_initializer="glorot_uniform")(x)
x = keras.layers.Dense(8, activation="relu", kernel_initializer="glorot_uniform")(x)
x = keras.layers.Dense(8, activation="relu", kernel_initializer="glorot_uniform")(x)
benzene = keras.layers.Dense(3, activation="linear", kernel_initializer="glorot_uniform")(x)
model = Model(inputs=[readings], outputs=[benzene])
model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
model = Model(inputs=[readings], outputs=[benzene])
model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
NUM_EPOCHS = 8000
BATCH_SIZE = 200
folder = "/Users/HRainX/Desktop"
Xtrain = pd.read_csv(os.path.join(folder, 'Xtrain.csv'))
Ytrain = pd.read_csv(os.path.join(folder, 'Ytrain.csv'))
history = model.fit(Xtrain, Ytrain,
batch_size=BATCH_SIZE,
epochs=NUM_EPOCHS,
validation_split=0.2)
model.save('model.h5')
这是完整代码