How can I add CSV logging mechanism in case of Multivariable Linear Regression using TensorFlow?-CodePudding

Suppose, the following is my Multivariable Linear Regression source code in Python:

import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

import sys, random
import time

import tensorflow as tf
from   tensorflow import keras
from   tensorflow.keras.models import Sequential
from   tensorflow.keras.layers import Dense
from   tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np

def load_data_k(fname: str, yyy_index: int, **selection):
    i = 0
    file = open(fname)
    if "top_n_lines" in selection:
        lines = [next(file) for _ in range(int(selection["top_n_lines"]))]
    elif "random_n_lines" in selection:
        tmp_lines = file.readlines()
        lines = random.sample(tmp_lines, int(selection["random_n_lines"]))
    else:
        lines = file.readlines()

    data_x, data_y = [], []
    for l in lines:
        row = l.strip().split()   
        x = [float(ix) for ix in row[yyy_index 1:]] 
        y = float(row[yyy_index])  
        data_x.append(x) 
        data_y.append(y) 
    # END for l in lines...

    num_rows = len(data_x)

    print("row size = ", len(data_x[0]))

    given_fraction = selection.get("validation_part", 1.0)
    if given_fraction > 0.9999:
        valid_x, valid_y = data_x, data_y
    else:
        n = int(num_rows * given_fraction)
        data_x, data_y = data_x[n:], data_y[n:]
        valid_x, valid_y = data_x[:n], data_y[:n]
    # END of if-else block

    print("size of x = ", len(data_x))
    print("size of y = ", len(data_y))

    tx = tf.convert_to_tensor(data_x, dtype=tf.float32)
    ty = tf.convert_to_tensor(data_y, dtype=tf.float32)

    vx = tf.convert_to_tensor(valid_x, dtype=tf.float32)
    vy = tf.convert_to_tensor(valid_y, dtype=tf.float32)

    return tx, ty, vx, vy
# END of the function


# load training data from the disk
train_x, train_y, validate_x, validate_y = \
        load_data_k(
            fname="data_file.csv",
            yyy_index=6,
            random_n_lines=90000,
            validation_part=0.2
        )

print("training data size : ", len(train_x))
print("validation data size : ", len(validate_x))

predict_data = np.array([[7.042, 5.781, 5.399, 5.373, 5.423, -9.118, 5.488,   5.166,   4.852,  7.470,  6.452,  6.069,
                         0,     0,     0,     1,     0,      1,     1,       3,       0,      0,      0,      1, 
                         0,     0,     0,     0,     0,      0,     0,       0,       0,      0,      0,      0, 
                         0,     0,     0,     0]])


# Create Keras model
model = Sequential()
model.add(Dense(1, input_dim=40))
model.add(Dense(128))
model.add(Dense(128))
model.add(Dense(1))

# Gradient descent algorithm
adam_opt = Adam(0.1)

model.compile(loss='mse', optimizer=adam_opt)

history = model.fit(train_x, train_y, epochs=500)

prediction = model.predict(predict_data)
print(prediction)

I want to add CSV logging for the training loss, validation loss, training accuracy, and validation accuracy.

How can I do that?

CodePudding user response：

Just use tf.keras.callbacks.CSVLogger and any regression metric you want to log during training:

import tensorflow as tf

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1, input_dim=40))
model.add(tf.keras.layers.Dense(128))
model.add(tf.keras.layers.Dense(128))
model.add(tf.keras.layers.Dense(1))

adam_opt = tf.keras.optimizers.Adam(0.1)
model.compile(loss='mse', optimizer=adam_opt, metrics=tf.keras.metrics.MeanSquaredError(name="mean_squared_error", dtype=None))

train_x = tf.random.normal((50, 40))
train_y = tf.random.normal((50, 1))
val_x = tf.random.normal((50, 40))
val_y = tf.random.normal((50, 1))

csv_logger = tf.keras.callbacks.CSVLogger('model_training.csv')
history = model.fit(train_x, train_y, epochs=5, validation_data=(val_x, val_y), callbacks=[csv_logger])

model_training.csv:

epoch        loss  mean_squared_error    val_loss  val_mean_squared_error
    0  304.349060          304.349060   69.584991               69.584991
    1  105.304787          105.304787  170.063126              170.063126
    2  175.232788          175.232788    7.874812                7.874812
    3  104.159607          104.159607  320.626556              320.626556
    4  194.709763          194.709763    1.438866                1.438866