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EricSchles/tensorflow_nn_auto_ml_prototype.py

Created November 3, 2020 21:48
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tensorflow_nn_auto_ml_prototype.py
from tensorflow.keras import Model
import tensorflow as tf
import numpy as np
import pandas as pd
import random
class ReluDense(tf.Module):
def __init__(self, in_features, out_features, name=None):
super().__init__(name=name)
self.w = tf.Variable(
tf.random.normal([out_features, out_features]), name='w'
)
self.b = tf.Variable(
tf.zeros([out_features]), name='b'
)
def __call__(self, x):
y = tf.matmul(x, self.w) + self.b
return tf.nn.relu(y)
class Dense(tf.Module):
def __init__(self, in_features, out_features, name=None):
super().__init__(name=name)
self.w = tf.Variable(
tf.random.normal([out_features, out_features]), name='w'
)
self.b = tf.Variable(
tf.zeros([out_features]), name='b'
)
def __call__(self, x):
return tf.matmul(x, self.w) + self.b
class NeuralNet(Model):
def __init__(self, X_in, X_out, optimizer, dropout=0.1):
super(NeuralNet, self).__init__()
self.number_of_relu_dense = 0
self.number_of_vanilla_dense = 0
self.relu_layers = []
self.dense_layers = []
for _ in range(1, random.randint(2, 10)):
self.relu_layers.append(ReluDense(X_in, X_out))
self.number_of_relu_dense += 1
for _ in range(1, random.randint(2, 10)):
self.dense_layers.append(Dense(X_in, X_out))
self.number_of_vanilla_dense += 1
self.out = Dense(X_in, X_out)
self.optimizer = optimizer
self.dropout = dropout
def call(self, x, train=False):
if train:
for layer in self.relu_layers:
x = layer(x)
x = dropout(x, self.dropout)
for layer in self.dense_layers:
x = layer(x)
x = dropout(x, self.dropout)
else:
for layer in self.relu_layers:
x = layer(x)
x = dropout(x, self.dropout)
for layer in self.dense_layers:
x = layer(x)
x = dropout(x, self.dropout)
x = self.out(x)
return tf.reduce_mean(x, axis=1)
def step(self, x, y):
x = tf.cast(x, tf.float32)
y = tf.cast(y, tf.float32)
with tf.GradientTape() as tape:
pred = self.call(x)
loss = mse(pred, y)
gradients = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))
def dropout(X, drop_probability):
keep_probability = 1 - drop_probability
mask = np.random.uniform(0, 1.0) < keep_probability
if keep_probability > 0.0:
scale = (1/keep_probability)
else:
scale = 0.0
return mask * X * scale
def mse(x, y):
x = tf.cast(x, tf.float64)
y = tf.cast(y, tf.float64)
return tf.metrics.MSE(x, y)
def maape(y, y_pred):
normed_absolute_error = np.abs(y_pred - y) / y
normed_arctan_abs_error = np.arctan(normed_absolute_error)
return np.sum(normed_arctan_abs_error)
if __name__ == '__main__':
X = pd.read_csv("X.csv")
y = np.load("y.npy")
X = X.values
X_val = pd.read_csv("X_val.csv")
X_val = X_val.values
y_val = np.load("y_val.npy")
mse_loss = []
maape_loss = []
stopped = []
optimizer = tf.optimizers.Adam(0.9)
neural_nets = [NeuralNet(X.shape[0], X.shape[1], optimizer)
for _ in range(50)]
for step in range(300):
for index in range(len(neural_nets)):
if index in stopped:
continue
nn = neural_nets[index]
nn.step(X, y)
pred = nn(X_val)
loss_mse = mse(pred, y_val)
loss_maape = maape(pred, y_val)
is_nan = pd.isnull(loss_mse.numpy()) or pd.isnull(loss_maape)
is_less_than_zero = loss_mse.numpy() < 0 or loss_maape < 0
if is_nan or is_less_than_zero:
stopped.append(index)
continue
mse_loss.append(loss_mse)
maape_loss.append(loss_maape)
min_index = mse_loss.index(min(mse_loss))
print(mse_loss[min_index].numpy())
print(maape_loss[min_index])
print(len(stopped))
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