Exemple #1
0
def get_approximate_options(n, m, methods, losses):
    #X, y = load_boston(return_X_y=True)

    #x = X[:100]
    #x_validate = X[420:]
    x = [[y] * m for y in range(1, 25)]
    x_validate = [[y + 0.5] * m for y in range(0, 25)]
    #x = [[y] * m for y in range(1, 101)]
    #x_validate = [[y + 0.5] * m for y in range(0, 100)]
    #X, y = load_diabetes(return_X_y=True)

    #x = X[:50]
    #x_validate = X[400:]
    options = []
    for method in methods:
        options.append({
            "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x],
            "x_validate": [
                convert_variables_without_trainable(x_tmp)
                for x_tmp in x_validate
            ],
            "params":
            convert_variables([1 for x in range(n)]),
            "loss_function":
            losses,
            "opt":
            method,
            "eps":
            0.0001,
            "max_steps":
            100,
        })
    return options
Exemple #2
0
from utils.generate_data import generate_x
from utils.utils import convert_variables, convert_variables_without_trainable
from utils.generate_data import generate_set

from sklearn.datasets import load_boston

X, y = load_boston(return_X_y=True)

x = X[:100]
x_validate = X[420:]


approximate_options9_3 = [
    {
        "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x],
        "x_validate": [convert_variables_without_trainable(x_tmp) for x_tmp in x_validate],
        "params": convert_variables([5 for x in range(79)]),
        "loss_function": tf.keras.losses.MSE,
        "opt": tf.keras.optimizers.SGD(learning_rate=1),
        "eps": 0.0001,
        "max_steps": 30,
    },
    {
        "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x],
        "x_validate": [convert_variables_without_trainable(x_tmp) for x_tmp in x_validate],
        "params": convert_variables([5 for x in range(79)]),
        "loss_function": tf.keras.losses.MSE,
        "opt": tf.keras.optimizers.SGD(learning_rate=0.1),
        "eps": 0.0001,
        "max_steps": 30,
Exemple #3
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import tensorflow as tf

from utils.utils import convert_variables, convert_variables_without_trainable
"""
Approximate options
"""

approximate_options2 = [
    {
        "x": [
            convert_variables_without_trainable([x, x])
            for x in range(-10, 10, 1)
        ],
        "x_validate": [
            convert_variables_without_trainable([x, x])
            for x in range(-11, 10, 1)
        ],
        "params":
        convert_variables([1 for x in range(11)]),
        "loss_function":
        tf.keras.losses.MAE,
        "opt":
        tf.keras.optimizers.Adam(learning_rate=0.001),
        "eps":
        0.0001,
        "max_steps":
        1000,
    },
    {
        "x": [
            convert_variables_without_trainable([x, x])
 def __init__(self):
     self.val = {}
     X, y = load_diabetes(return_X_y=True)
     for k, v in zip(X, y):
         self.val[str(convert_variables_without_trainable(k))] = v
Exemple #5
0
import numpy as np
import tensorflow as tf

from utils.utils import convert_variables, convert_variables_without_trainable

"""
Approximate options
"""

approximate_options1 = [
    {
        "x": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0, 5, 0.2))],
        "x_validate": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0.1, 5, 0.02))],
        "params": convert_variables([1 for x in range(11)]),
        "loss_function": tf.keras.losses.MAE,
        "opt": tf.keras.optimizers.Adam(learning_rate=0.001),
        "eps": 0.0001,
        "max_steps": 1000,
    },
    {
        "x": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0, 5, 0.2))],
        "x_validate": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0.1, 5, 0.02))],
        "params": convert_variables([1 for x in range(11)]),
        "loss_function": tf.keras.losses.MAE,
        "opt": tf.keras.optimizers.Adam(learning_rate=0.01),
        "eps": 0.0001,
        "max_steps": 10000,
    },
    {
        "x": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0, 5, 0.2))],
        "x_validate": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0.1, 5, 0.02))],