TensorFlow, PyTorch and Neural Designer are three popular machine learning platforms developed by Google, Facebook and Artelnics, respectively.
Although all that frameworks implement neural networks, they present some important differences in functionality, usability, performance, etc.
This post compares the training accuracy of TensorFlow, PyTorch, and Neural Designer for an approximation benchmark.
As we will see, the training accuracy of Neural Designer using the LevenbergMarquardt algorithm is x1.91 higher than that of TensorFlow and x1.21 times higher than that of PyTorch using Adam.
Moreover, Neural Designer trains this neural network x5.71 times faster than TensorFlow and x8.21 times faster than PyTorch.
In this article, we provide all the steps that you need to reproduce the results using the free trial of Neural Designer.
Contents:
One of the most critical factors in machine learning platforms is their training accuracy.
This article aims to measure the training accuracies of TensorFlow, PyTorch, and Neural Designer for a benchmark application and compare the speeds obtained by those platforms.
The most important factor for training accuracy is the optimization algorithm used.
The above table shows that TensorFlow and PyTorch are programmed in C++ and Python, while Neural Designer is entirely programmed in C++.
Next, we measure the training accuracy for a benchmark problem on a reference computer using TensorFlow, PyTorch, and Neural Designer. We then compare the results produced by that platforms.
The first step is to choose a benchmark application that is general enough to conclude the performance of the machine learning platforms. As previously stated, we will train a neural network that approximates a set of inputtarget samples.
In this regard, an approximation application comprises a data set, a neural network, and an associated training strategy. The next table uniquely defines these three components.
Data set 


Neural network 

Training strategy 

Once we have created the TensorFlow, PyTorch, and Neural Designer applications, we need to run them.
The next step is to choose the computer to train the neural networks with TensorFlow, PyTorch, and Neural Designer.
Operating system:  Windows 10 Enterprise 

Processor:  CPU Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 
Physical RAM:  16.0 GB 
Once the computer has been chosen, we install TensorFlow (2.1.0), PyTorch (1.7.0), and Neural Designer (5.9.0) on it.
#TENSORFLOW CODE import tensorflow as tf import pandas as pd import time import numpy as np #read data float32 start_time = time.time() filename = "C:/R_new.csv" df_test = pd.read_csv(filename, nrows=100) float_cols = [c for c in df_test if df_test[c].dtype == "float64"] float32_cols = {c: np.float32 for c in float_cols} data = pd.read_csv(filename, engine='c', dtype=float32_cols) print("Loading time: ", round(time.time()  start_time), " seconds") x = data.iloc[:,:1].values y = data.iloc[:,[1]].values initializer = tf.keras.initializers.RandomUniform(minval=1., maxval=1.) #build model model = tf.keras.models.Sequential([tf.keras.layers.Dense(1000, activation = 'tanh', kernel_initializer = initializer, bias_initializer=initializer), tf.keras.layers.Dense(1, activation = 'linear', kernel_initializer = initializer, bias_initializer=initializer)]) #compile model model.compile(optimizer='adam', loss = 'mean_squared_error') #train model start_time = time.time() history = model.fit(x, y, batch_size = 1000, epochs = 1000) print("Training time: ", round(time.time()  start_time), " seconds")
Building this application with PyTorch also requires some Python scripting. This code is listed below. Also, you can download here.
#PYTORCH CODE import pandas as pd import time import torch import numpy as np import statistics def init_weights(m): if type(m) == torch.nn.Linear: torch.nn.init.uniform_(m.weight, a=1.0, b=1.0) torch.nn.init.uniform_(m.bias.data, a=1.0, b=1.0) epoch = 1000 total_samples, batch_size, input_variables, hidden_neurons, output_variables = 1000000, 1000, 1000, 1000, 1 device = torch.device("cuda:0") # read data float32 start_time = time.time() filename = "C:/R_new.csv" df_test = pd.read_csv(filename, nrows=100) float_cols = [c for c in df_test if df_test[c].dtype == "float64"] float32_cols = {c: np.float32 for c in float_cols} dataset = pd.read_csv(filename, engine='c', dtype=float32_cols) print("Loading time: ", round(time.time()  start_time), " seconds") x = torch.tensor(dataset.iloc[:,:1].values, dtype = torch.float32) y = torch.tensor(dataset.iloc[:,[1]].values, dtype = torch.float32) # build model model = torch.nn.Sequential(torch.nn.Linear(input_variables, hidden_neurons), torch.nn.Tanh(), torch.nn.Linear(hidden_neurons, output_variables)).cuda() # initialize weights model.apply(init_weights) # compile model learning_rate = 0.001 loss_fn = torch.nn.MSELoss(reduction = 'mean') optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) indices = np.arange(0,total_samples) start = time.time() for j in range(epoch): mse=[] t0 = time.time() for i in range(0, total_samples, batch_size): batch_indices = indices[i:i+batch_size] batch_x, batch_y = x[batch_indices], y[batch_indices] batch_x = batch_x.cuda() batch_y = batch_y.cuda() outputs = model.forward(batch_x) loss = loss_fn(outputs, batch_y) model.zero_grad() loss.backward() optimizer.step() mse.append(loss.item()) print("Epoch:", j+1,"/1000", "[================================]  ","loss: ", statistics.mean(mse)) t1 = time.time()  t0 print("Elapsed time: ", int(round(t1 )), "sec") end = time.time() elapsed = end  start print("Training time: ",int(round(elapsed )), "seconds")
Once the TensorFlow, PyTorch, and Neural Designer applications have been created, we need to run them.
The last step is to run the benchmark application on the selected machine with TensorFlow, PyTorch, and Neural Designer and compare those platforms' training times.
The next figure shows the training results with TensorFlow.
Run  Time  MSE 

1  00:47  0.0587 
2  00:48  0.0582 
3  00:48  0.0988 
4  00:47  0.1012 
5  00:47  0.0508 
6  00:48  0.1008 
7  00:51  0.0333 
8  00:52  0.0998 
9  00:50  0.0582 
10  00:48  0.0454 
As we can see, the minimum mean squared error by TensorFlow is 0.0333, and the average mean squared error over the ten runs is 0.0705. The average training time is 48.6 seconds.
Similarly, the following figure is a screenshot of PyTorch at the end of the process.
Run  Time  MSE 

1  01:15  0.0294 
2  01:09  0.0474 
3  01:10  0.0332 
4  01:08  0.0586 
5  01:10  0.0221 
6  01:09  0.0480 
7  01:12  0.1006 
8  01:10  0.0332 
9  01:09  0.0582 
10  01:06  0.0988 
In this case, the minimum mean squared error by PyTorch over the ten runs is 0.0221. The average mean squared error is 0.0529. The average training time is 69.8 seconds.
Finally, the following figure shows the training results with Neural Designer.
Run  Time  MSE 

1  00:08  0.0196 
2  00:09  0.0263 
3  00:08  0.0254 
4  00:09  0.0191 
5  00:09  0.0413 
6  00:09  0.0263 
7  00:08  0.0397 
8  00:08  0.0174 
9  00:08  0.0527 
10  00:09  0.0521 
The minimum mean squared error by Neural Designer is 0.0174. The average mean squared error over the ten runs is 0.0320. With Neural Designer, the average training time is 8.5 seconds.
The following table summarizes the metrics yield by the three machine learning platforms.
TensorFlow  PyTorch  Neural Designer  

Minimum MSE  0.0333  0.0221  0.0174 
Average MSE  0.0705  0.0529  0.0320 
Average training time  48.6 seconds  69.8 seconds  8.5 seconds 
Finally, the following chart depicts the training accuracies of TensorFlow, PyTorch, and Neural Designer for this case graphically.
As we can see, both the minimum and the average mean squared error of Neural Designer using the LM algorithm is smaller than that of TensorFlow and PyTorch using Adam.
Using these metrics, we can say that the precision of Neural Designer for this benchmark is x1.91 times bigger than that of TensorFlow and 1.27 times higher than that of PyTorch.
Regarding the training time, in this benchmark, Neural Designer is about x5.72 times faster than TensorFlow and x8.21 times faster than PyTorch.
Neural Designer implements secondorder optimizers, such as the quasiNewton method and the LevenbergMarquardt algorithm. These algorithms have better convergence properties for small and mediumsized datasets than firstorder optimizers, such as Adam.
This results in that, for the benchmark described in this post, the precision of Neural Designer is x1.91 times faster than that of TensorFlow and x1.27 times faster than that of PyTorch.
To reproduce these results, download the free trial of Neural Designer and follow the steps described in this article.