Modul 8 Praktikum Sains Data

pip install tensorflow

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Note: you may need to restart the kernel to use updated packages.


[notice] A new release of pip is available: 23.2.1 -> 24.0
[notice] To update, run: python.exe -m pip install --upgrade pip

Deep Learning Model untuk klasifikasi Fashion MNIST

Fashion MNIST adalah dataset dari Keras dan Tensorflow yang berisikan gambar-gambar berupa pakaian, sebanyak 70.000 buah dan ukuran setiap gambar adalah 28x28 pixel

import numpy as np
import matplotlib.pyplot as plt
import keras

from keras.datasets import fashion_mnist

(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
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Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
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print("X_train_full: ", X_train_full.shape)
print("y_train_full: ", y_train_full.shape)
print("X_test: ", X_test.shape)
print("y_test: ", y_test.shape)

X_train_full:  (60000, 28, 28)
y_train_full:  (60000,)
X_test:  (10000, 28, 28)
y_test:  (10000,)

X_train_full[0]

array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,
          0,   0,  13,  73,   0,   0,   1,   4,   0,   0,   0,   0,   1,
          1,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   3,
          0,  36, 136, 127,  62,  54,   0,   0,   0,   1,   3,   4,   0,
          0,   3],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   6,
          0, 102, 204, 176, 134, 144, 123,  23,   0,   0,   0,   0,  12,
         10,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0, 155, 236, 207, 178, 107, 156, 161, 109,  64,  23,  77, 130,
         72,  15],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   0,
         69, 207, 223, 218, 216, 216, 163, 127, 121, 122, 146, 141,  88,
        172,  66],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   1,   1,   0,
        200, 232, 232, 233, 229, 223, 223, 215, 213, 164, 127, 123, 196,
        229,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
        183, 225, 216, 223, 228, 235, 227, 224, 222, 224, 221, 223, 245,
        173,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
        193, 228, 218, 213, 198, 180, 212, 210, 211, 213, 223, 220, 243,
        202,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   3,   0,  12,
        219, 220, 212, 218, 192, 169, 227, 208, 218, 224, 212, 226, 197,
        209,  52],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   6,   0,  99,
        244, 222, 220, 218, 203, 198, 221, 215, 213, 222, 220, 245, 119,
        167,  56],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   4,   0,   0,  55,
        236, 228, 230, 228, 240, 232, 213, 218, 223, 234, 217, 217, 209,
         92,   0],
       [  0,   0,   1,   4,   6,   7,   2,   0,   0,   0,   0,   0, 237,
        226, 217, 223, 222, 219, 222, 221, 216, 223, 229, 215, 218, 255,
         77,   0],
       [  0,   3,   0,   0,   0,   0,   0,   0,   0,  62, 145, 204, 228,
        207, 213, 221, 218, 208, 211, 218, 224, 223, 219, 215, 224, 244,
        159,   0],
       [  0,   0,   0,   0,  18,  44,  82, 107, 189, 228, 220, 222, 217,
        226, 200, 205, 211, 230, 224, 234, 176, 188, 250, 248, 233, 238,
        215,   0],
       [  0,  57, 187, 208, 224, 221, 224, 208, 204, 214, 208, 209, 200,
        159, 245, 193, 206, 223, 255, 255, 221, 234, 221, 211, 220, 232,
        246,   0],
       [  3, 202, 228, 224, 221, 211, 211, 214, 205, 205, 205, 220, 240,
         80, 150, 255, 229, 221, 188, 154, 191, 210, 204, 209, 222, 228,
        225,   0],
       [ 98, 233, 198, 210, 222, 229, 229, 234, 249, 220, 194, 215, 217,
        241,  65,  73, 106, 117, 168, 219, 221, 215, 217, 223, 223, 224,
        229,  29],
       [ 75, 204, 212, 204, 193, 205, 211, 225, 216, 185, 197, 206, 198,
        213, 240, 195, 227, 245, 239, 223, 218, 212, 209, 222, 220, 221,
        230,  67],
       [ 48, 203, 183, 194, 213, 197, 185, 190, 194, 192, 202, 214, 219,
        221, 220, 236, 225, 216, 199, 206, 186, 181, 177, 172, 181, 205,
        206, 115],
       [  0, 122, 219, 193, 179, 171, 183, 196, 204, 210, 213, 207, 211,
        210, 200, 196, 194, 191, 195, 191, 198, 192, 176, 156, 167, 177,
        210,  92],
       [  0,   0,  74, 189, 212, 191, 175, 172, 175, 181, 185, 188, 189,
        188, 193, 198, 204, 209, 210, 210, 211, 188, 188, 194, 192, 216,
        170,   0],
       [  2,   0,   0,   0,  66, 200, 222, 237, 239, 242, 246, 243, 244,
        221, 220, 193, 191, 179, 182, 182, 181, 176, 166, 168,  99,  58,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,  40,  61,  44,  72,  41,  35,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0]], dtype=uint8)

sample = 0
image = X_train_full[sample]

plt.imshow(image, cmap = 'binary')
plt.show()

print("kelas untuk gambar indeks ke-0: ", y_train_full[sample])

kelas untuk gambar indeks ke-0:  9

# Deskripsi untuk setiap kelas
class_mnist = ['T-Shirt/Top', 'Trouser', 'Pullover', 'Dress', 'Coat', 
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot']

#menampilkan beberapa image dari fashion_mnist
num_row = 3
num_col = 7
num = num_row * num_col

images = X_train_full[:num]
labels = y_train_full[:num]

fig, axes = plt.subplots(num_row, num_col, figsize = (1.5*num_col, 2*num_row))
for i in range(num):
    ax = axes[i//num_col, i%num_col]
    ax.imshow(images[i], cmap = 'binary')
    ax.set_title(f'{class_mnist[labels[i]]}')
plt.tight_layout()
plt.show()

Membuat Model

Langkah-langkah dalam membangun model:

Spesifikasi arsitekturnya
Compile
Fit
Prediksi

#Data dibagi menjadi data train, test, dan validasi
#Data validasi diambil dari 5000 data pertama dari data train full
#Lalu diskalakan dari 0 - 1

X_valid, X_train = X_train_full[:5000]/255.0, X_train_full[5000:]/255.0
y_valid, y_train = y_train_full[:5000], y_train_full[5000:]

print("X_train: ", X_train.shape)
print("y_train: ", y_train.shape)
print("X_valid: ", X_valid.shape)
print("y_valid: ", y_valid.shape)
print("X_test: ", X_test.shape)
print("y_test: ", y_test.shape)

X_train:  (55000, 28, 28)
y_train:  (55000,)
X_valid:  (5000, 28, 28)
y_valid:  (5000,)
X_test:  (10000, 28, 28)
y_test:  (10000,)

model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape = [28, 28])) #mengubah input menjadi 1D array
model.add(keras.layers.Dense(300, activation = 'relu')) #hidden layer dgn 300 neuron dan activation ReLu
model.add(keras.layers.Dense(100, activation = 'relu')) #hidden layer dgn 100 neuron dan activation ReLu
model.add(keras.layers.Dense(10, activation = 'softmax')) #output layer dgn 10 neuron dan activation softmax

c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\layers\reshaping\flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)

model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ flatten (Flatten)               │ (None, 784)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 300)            │       235,500 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 100)            │        30,100 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 10)             │         1,010 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 266,610 (1.02 MB)

 Trainable params: 266,610 (1.02 MB)

 Non-trainable params: 0 (0.00 B)

Loss Function

Nilai akumulasi error dari seluruh data prediksi

Contoh: - MSE (Mean Squared Error) pada masalah Regresi - Sparse_Categorical_Entropy pada masalah klasifikasi pada 2 atau lebih label kelas

Digunakan untuk mengukur performa model

model.compile(loss = 'sparse_categorical_crossentropy',
              optimizer = 'sgd', #stocastic gradient descent
              metrics = ['accuracy'])

#fit the model
history = model.fit(X_train, y_train, epochs = 10,
                    validation_data = (X_valid, y_valid))

Epoch 1/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 9s 5ms/step - accuracy: 0.6836 - loss: 0.9952 - val_accuracy: 0.8294 - val_loss: 0.5058
Epoch 2/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 6s 4ms/step - accuracy: 0.8253 - loss: 0.5001 - val_accuracy: 0.8390 - val_loss: 0.4606
Epoch 3/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 6s 3ms/step - accuracy: 0.8429 - loss: 0.4473 - val_accuracy: 0.8582 - val_loss: 0.4038
Epoch 4/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 6s 3ms/step - accuracy: 0.8537 - loss: 0.4158 - val_accuracy: 0.8640 - val_loss: 0.3900
Epoch 5/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 11s 3ms/step - accuracy: 0.8573 - loss: 0.4013 - val_accuracy: 0.8708 - val_loss: 0.3719
Epoch 6/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 6s 3ms/step - accuracy: 0.8674 - loss: 0.3779 - val_accuracy: 0.8702 - val_loss: 0.3795
Epoch 7/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 5s 3ms/step - accuracy: 0.8701 - loss: 0.3676 - val_accuracy: 0.8644 - val_loss: 0.3773
Epoch 8/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 6s 4ms/step - accuracy: 0.8756 - loss: 0.3541 - val_accuracy: 0.8766 - val_loss: 0.3541
Epoch 9/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 7s 4ms/step - accuracy: 0.8759 - loss: 0.3435 - val_accuracy: 0.8704 - val_loss: 0.3550
Epoch 10/10
1719/1719 ━━━━━━━━━━━━━━━━━━━━ 11s 4ms/step - accuracy: 0.8818 - loss: 0.3361 - val_accuracy: 0.8700 - val_loss: 0.3585

Menggunakan Model untuk Prediksi

model.evaluate(X_test, y_test)

313/313 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.8310 - loss: 66.4840

[69.83614349365234, 0.8278999924659729]

X_new = X_test[:5]
y_proba = model.predict(X_new)

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 216ms/step

y_proba #mengeluarkan peluang suatu data masuk ke kelas tertentu

array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.]], dtype=float32)

y_p = np.array([np.argmax(i) for i in y_proba]) #mendapatkan kelas
print(y_p)
print(np.array(class_mnist)[y_p]) #deskripsi kelasnya

[9 2 1 1 6]
['Ankle Boot' 'Pullover' 'Trouser' 'Trouser' 'Shirt']

y_test[:5] #data yang asli

array([9, 2, 1, 1, 6], dtype=uint8)