no decrease loss and val_loss

I try to train a neural network for time series. I use some data from Covid, mainly the goal is knowing 14 days of number of people at hospital to predict the number at J+1. I have use some early stopping to not over fit, but almost one time over two the learning stop at patience+1 and there is no decrease of loss and val_loss. I have tries to move hyperparameters like learning rate but the problem is always here. Any guess?

The main code is below and the whole code with data :https://github.com/paullaurain/prediction

import os
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
from keras.optimizers import Adam
from sklearn.preprocessing import MinMaxScaler

# fit a model
def model_fit(data, config):
# unpack config
    n_in,n_out, n_nodes, n_epochs, n_batch,p,pl = config
# prepare data
    DATA = series_to_supervised(data, n_in, n_out)
    X, Y = DATA[:, :-n_out], DATA[:, n_in:]
    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.1)
# define model
    model = Sequential()
    model.add(Dense(4*n_nodes, activation= 'relu', input_dim=n_in))
    model.add(Dense(2*n_nodes, activation= 'relu'))
    model.add(Dense(n_nodes, activation= 'relu'))
    model.add(Dense(n_out, activation= 'relu'))
    model.compile(loss='mse' , optimizer='adam',metrics=['mse'])
# fit
    es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=p)
    file='best_modelDense.hdf5'
    mc = ModelCheckpoint(filepath=file, monitor='loss', mode='min', verbose=0, save_best_only=True)
    history=model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs=n_epochs, verbose=0,batch_size=n_batch, callbacks=[es,mc])
    if pl:
        plt.plot(history.history['loss'])
        plt.plot(history.history['val_loss'])
        plt.title('model loss')
        plt.ylabel('loss')
        plt.xlabel('epoch')
        plt.legend(['train', 'test'], loc='upper left')
        plt.show()
    saved_model=load_model(file)
    os.remove(file)
    return history.history['val_loss'][-p], saved_model

# repeat evaluation of a config
def repeat_evaluate(data,n_test, config, n_repeat,plot):
    # rescale data
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaler = scaler.fit(data)
    scaled_data=scaler.transform(data)
    scores=[]
    for _ in range(n_repeat):
        score, model= model_fit(scaled_data[:-n_test], config)
        scores.append(score)
    # plot the prediction id asked
        if plot:
            y=[]
            x=[]
            for i in range(n_test,0,-1):
                y.append(float(model.predict(scaled_data[-14-i:-i].reshape(1,14))))
                x.append(scaled_data[-i])
            X=scaler.inverse_transform(x)
            plt.plot(X)
            Y=scaler.inverse_transform(np.array([y]))
            plt.plot(Y.reshape(10,1))
            plt.title('result')
            plt.legend(['real', 'prdiction'], loc='upper left')
        
            plt.show()
    return scores

# summarize model performance
def summarize_scores(name, scores):
# print a summary
    scores_m, score_std = mean(scores), std(scores)
    print( '%s: %.3f RMSE (+/- %.3f)' % (name, scores_m, score_std))
    # box and whisker plot
    pyplot.boxplot(scores)
    pyplot.show()
    
#setting variable
n_in=14
n_out=1
n_repeat=5
n_test=10
# define config n_in, n_out, n_nodes, n_epochs, n_batch, pateince, draw loss
config = [n_in, n_out, 10, 2000, 50, 200,True]
# compute scores
scores = repeat_evaluate(data,n_test, config, n_repeat, True)
print(scores)
# summarize scores
summarize_scores('mlp ', scores)

Result:

from no decrease loss and val_loss