how to convert python code to MATLAB?

Is there way to convert this python code to matlab code?

it's too hard to me :(

how to convert python to matlab???

this is code what I want to convert.

from sklearn.model_selection import train_test_split
import keras
from keras.models import Sequential
from keras.layers import Dense 
import numpy as np

np.random.seed(3)

# number of wine classes
classifications = 3

# load dataset
dataset = np.loadtxt('wine.csv', delimiter=",")

# split dataset into sets for testing and training
X = dataset[:,1:14]
Y = dataset[:,0:1]
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.66, random_state=5)

# convert output values to one-hot
y_train = keras.utils.to_categorical(y_train-1, classifications)
y_test = keras.utils.to_categorical(y_test-1, classifications)

# creating model
model = Sequential()
model.add(Dense(10, input_dim=13, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(2, activation='relu'))
model.add(Dense(classifications, activation='softmax'))

# compile and fit model
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=15, epochs=2500, validation_data=(x_test, y_test))

please!

[GT]

To the best of my knowledge there is no "automatic" python to MATLAB converter. There are a couple of things you can do:

• Take your Python code and invoke it in MATLAB (yes, MATLAB can call Python code: https://nl.mathworks.com/help/matlab/call-python-libraries.html?searchHighlight=python&s_tid=doc_srchtitle )
• Given that your Python program depends on Keras, and numpy you would need to know exactly what these are doing. Typically MATLAB byitself has everything that numpy does but the Keras part I am not sure how you would translate it other than using the Deep Learning Toolbox: https://nl.mathworks.com/help/deeplearning/examples/assemble-network-from-pretrained-keras-layers.html?searchHighlight=keras&s_tid=doc_srchtitle

Hope that this helps

Answer 1

[Abderrahmane Bakhouche]

# Metamodel regression

X_train, X_test, y_train, y_test = \

train_test_split(LDB1.iloc[:,:-1], LDB1["d"], test_size=0.4, random_state=42)

clf = make_pipeline(SplineTransformer(),

MLPRegressor(alpha=0.0001, hidden_layer_sizes = (20, 10), max_iter = 500000,

activation = 'relu', verbose = 'True', learning_rate_init=0.01))

a = clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

plt.figure()

# plt.scatter(X_train[P])

plt.scatter(X_test["P"], y_test.tolist(), label="Test values")

plt.scatter(X_test["P"], y_pred, label="Predicted values") # plot network output

plt.title("P vs d (Predicted and test values")

plt.legend()

Answer 2

[David Willingham]

Hi Min-seok Kim,

For Deep Learning there are a few ways to import and export networks into MATLAB.

MATLAB has a direct Tensorflow Importer you could use to import the network:

https://www.mathworks.com/help/deeplearning/ref/importtensorflownetwork.html

For other frameworks, you can import and export via ONNX:

https://www.mathworks.com/help/deeplearning/ref/importonnxnetwork.html

https://www.mathworks.com/help/deeplearning/ref/exportonnxnetwork.html

Regards,

David Willingham

Deep Learning Product Manager, MathWorks