Tensorflow simple neural network has very bad performance in regression (curve fitting) problem

I'm trying to implement a very simple one layered MLP for a toy regression problem with one variable (dimension = 1) and one target (dimension = 1). It's a simple curve fitting problem with zero noise.

Matlab - Deep Learning Toolbox

Using levenberg-marquardt backpropagation on a MLP with a single hidden layer with 100 neurons and hyperbolic tangent activation I got pretty decent performance with almost zero effort:

MSE = 7.18e-08

Here's a plot of the fitting:

Edit: This is the working matlab code. Please note that the "feedforwardnet(100)" function only produces a network object with one hidden layer with 100 neurons and tanh activation and output layer with linear activation:

net = feedforwardnet(100);
net.trainParam.min_grad = 1e-25;
net.trainParam.max_fail = 50;
net.trainParam.epochs = 500;
%net1.trainParam.showWindow = false;
net.inputs{1,1}.processFcns = {};
net.outputs{1,2}.processFcns = {};
net = train(net,Train_Vars,Train_Target);
Test_Predictions = net(Test_Vars);
Accuracy = msemetric({Test_Predictions},{Test_Target});

Python - TensorFlow - Keras

With the same network settings I used in matlab there's almost no training. No matter how hard I try to tune the training parameters or switch the optimizer.

MSE = 0.12900154

I can obtain something better using RELU activations for the hidden layer but we're still far:

MSE = 0.0582045

This is the code I used in Python:

#  IMPORT LIBRARIES
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras


#  IMPORT DATASET FROM CSV FILE, SHUFFLE TRAINING SET 
#  AND MAKE NUMPY ARRAY FOR TRAINING (DATA ARE ALREADY NORMALIZED)
dataset_path = "C:/Users/Rob/Desktop/Learning1.csv"
Learning_Dataset = pd.read_csv(dataset_path
                          , comment='\t',sep=","
                          ,skipinitialspace=False)
Learning_Dataset = Learning_Dataset.sample(frac = 1)  # SHUFFLING


test_dataset_path = "C:/Users/Rob/Desktop/Test1.csv"
Test_Dataset = pd.read_csv(test_dataset_path
                          , comment='\t',sep=","
                          ,skipinitialspace=False)


Learning_Target = Learning_Dataset.pop('Target')
Test_Target = Test_Dataset.pop('Target')

Learning_Dataset = np.array(Learning_Dataset,dtype = "float32")
Test_Dataset = np.array(Test_Dataset,dtype = "float32")
Learning_Target = np.array(Learning_Target,dtype = "float32")
Test_Target = np.array(Test_Target,dtype = "float32")





#  DEFINE SIMPLE MLP MODEL
inputs = tf.keras.layers.Input(shape=(1,))
x = tf.keras.layers.Dense(100, activation='relu')(inputs)
y = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs=inputs, outputs=y)




#  TRAIN MODEL
opt = tf.keras.optimizers.RMSprop(learning_rate = 0.001,
                                  rho = 0.9,
                                  momentum = 0.0,
                                  epsilon = 1e-07,
                                  centered = False)
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=100)
model.compile(optimizer = opt,
              loss = 'mse',
              metrics = ['mse'])


model.fit(Learning_Dataset, 
          Learning_Target,  
          epochs=500, 
          validation_split = 0.2, 
          verbose=0, 
          callbacks=[early_stop], 
          shuffle = False, 
          batch_size = 100)




#  INFERENCE AND CHECK ACCURACY
Predictions = model.predict(Test_Dataset)
Predictions = Predictions.reshape(10000)

print(np.square(np.subtract(Test_Target,Predictions)).mean()) #  MSE

plt.plot(Test_Dataset,Test_Target,'o',Test_Dataset,Predictions,'o')
plt.legend(('Target','Model Prediction'))
plt.show()

What am i doing wrong?

Thanks