I am training a simple machine learning model that takes a 1D description of a physical system (502 elements) and predicts the total energy (1 element). As I am new to TensorFlow I have used a simple dense neural network with two hidden layers of 64 neurons each:
Model: "total_energy"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
charge_density_x_max (InputL [(None, 502)] 0
_________________________________________________________________
hidden_1 (Dense) (None, 64) 32192
_________________________________________________________________
hidden_2 (Dense) (None, 64) 4160
_________________________________________________________________
dense (Dense) (None, 1) 65
=================================================================
Total params: 36,417
Trainable params: 36,417
Non-trainable params: 0
_________________________________________________________________
This is my source code for the training, evaluation and prediction:
# imports
import os
import ast
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
# load the dataset from the csv file
data = pd.read_csv('1e_data.csv')
# load in the data
x_train = np.zeros(shape=(600, 502))
x_test = np.zeros(shape=(400, 502))
y_train = np.zeros(shape=(600))
y_test = np.zeros(shape=(400))
for i in range(0, 1000):
if i < 600:
x_train[i,:] = np.append(np.array(ast.literal_eval(data.loc[i,'n'])), float(data.loc[i,'xmax']))
y_train[i] = float(data.loc[i,'E'])
else:
x_test[i-600,:] = np.append(np.array(ast.literal_eval(data.loc[i,'n'])), float(data.loc[i,'xmax']))
y_test[i-600] = float(data.loc[i,'E'])
# build the neural network model
inputs = tf.keras.Input(shape=(502,), name='charge_density_x_max')
hidden1 = tf.keras.layers.Dense(64, activation='sigmoid', name='hidden_1')(inputs)
hidden2 = tf.keras.layers.Dense(64, activation='sigmoid', name='hidden_2')(hidden1)
outputs = tf.keras.layers.Dense(1)(hidden2)
model = tf.keras.Model(inputs=inputs, outputs=outputs, name='total_energy')
# save the info of the model
with open('model_info.dat','w') as fh:
model.summary(print_fn=lambda x: fh.write(x + '\n'))
# compile the model
model.compile(optimizer='adam', loss='mean_absolute_percentage_error', metrics=['accuracy'])
# perform the training
model.fit(x_train, y_train, epochs=10)
# evaluate the model for accuracy
model.evaluate(x_test, y_test, verbose=2)
Yet when I run this it seems to do no training at all, giving an accuracy of 0.0000e+00:
Epoch 1/10
600/600 [==============================] - 0s 196us/sample - loss: 289.0616 - acc: 0.0000e+00
Epoch 2/10
600/600 [==============================] - 0s 37us/sample - loss: 144.5967 - acc: 0.0000e+00
Epoch 3/10
600/600 [==============================] - 0s 46us/sample - loss: 97.2109 - acc: 0.0000e+00
Epoch 4/10
600/600 [==============================] - 0s 46us/sample - loss: 108.0698 - acc: 0.0000e+00
Epoch 5/10
600/600 [==============================] - 0s 47us/sample - loss: 84.5921 - acc: 0.0000e+00
Epoch 6/10
600/600 [==============================] - 0s 38us/sample - loss: 79.9309 - acc: 0.0000e+00
Epoch 7/10
600/600 [==============================] - 0s 38us/sample - loss: 80.6755 - acc: 0.0000e+00
Epoch 8/10
600/600 [==============================] - 0s 47us/sample - loss: 87.5954 - acc: 0.0000e+00
Epoch 9/10
600/600 [==============================] - 0s 46us/sample - loss: 73.6634 - acc: 0.0000e+00
Epoch 10/10
600/600 [==============================] - 0s 38us/sample - loss: 78.0825 - acc: 0.0000e+00
400/400 - 0s - loss: 70.3813 - acc: 0.0000e+00
I have probably made a simple mistake here, but I do not know how to begin debugging. This should perform at least some training, but at the moment it seems to just skip the training and give an accuracy of 0.