{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"fashion-mnist.ipynb","provenance":[],"authorship_tag":"ABX9TyNc9rxn7RHIb1T1Zm0r0yCm"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"MDLLRVVZA9EW"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leanhducprovn/cdn/blob/master/tlu/elearning/machine-learning/fashion-mnist/fashion-mnist.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"dLMxBwZIUk5e"},"source":["# **Machine Learning Project**\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"tu82ej3SLoXp"},"source":["## Classify Fashion-MNIST (phân loại thời trang)"]},{"cell_type":"markdown","metadata":{"id":"wGHQ5AJDLYUW"},"source":["### Sinh viên thực hiện:"]},{"cell_type":"markdown","metadata":{"id":"zqtAFmv8L8zX"},"source":["* A36000 - Lê Anh Đức\n","* A35981 - Nguỵ Thị Ngọc Oanh\n","* A34684 - Phan Văn Hưng\n","* A37181 - Phạm Trung Tá"]},{"cell_type":"markdown","metadata":{"id":"OJTv6qaKLdEp"},"source":["### Giáo viên hướng dẫn:"]},{"cell_type":"markdown","metadata":{"id":"8eViz0FaMCWA"},"source":["* CTI023 - Nguyễn Công Điều\n","* CTI041 - Nguyễn Tú Anh"]},{"cell_type":"markdown","metadata":{"id":"Mg6s0mvmlMH7"},"source":["### Tổng quan"]},{"cell_type":"markdown","metadata":{"id":"QPg-uN7XMOve"},"source":["**Fashion MNIST** là tập dữ liệu được dùng để thay thế cho tập dữ liệu **MNIST kinh điển** thường dùng cho các chương trình \"Hello, World\" của machine learning trong lĩnh vực thị giác máy tính. Tập dữ liệu kinh điển vừa đề cập gồm ảnh của các con số (ví dụ 0, 1, 2) được viết tay. Các ảnh này có cùng định dạng tệp và độ phân giải với các ảnh về quần áo và giầy dép chúng ta sắp dùng.\n","\n","Với tập dữ liệu này, **60.000** ảnh sẽ được dùng để huấn luyện và **10.000** ảnh sẽ thường dùng để đánh giá khả năng phân loại nhận diện ảnh của mạng neuron.\n","\n","Mỗi ảnh là một mảng NumPy 2 chiều, 28x28, với mỗi pixel có giá trị từ 0 đến 255.\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n","
\n"," \"Fashion\n","
\n"," Figure 1. Fashion-MNIST samples\n","
"]},{"cell_type":"markdown","metadata":{"id":"haODX3PNK8iy"},"source":["#### Mô tả nhãn\n","Nhãn là một mảng của các số nguyên từ 0 đến 9, tương ứng với mỗi lớp quần áo giày dép:\n"," \n","\t \n","\t\n","\t\t\n","\t\t\n","\t\n","\t\n","\t\t\n","\t\t\n","\t\n","\t\n","\t\t\n","\t\t\n","\t\n","\t\n","\t\t\n","\t\t\n","\t\n","\t\n","\t\t\n","\t\t\n","\t\n","\t\n","\t\t\n","\t\t\n","\t\n","\t\n","\t\t\n","\t\t\n","\t\n","\t\n","\t\t\n","\t\t\n","\t\n","\t\n","\t\t\n","\t\t\n","\t\n","\t\n","\t\t\n","\t\t\n","\t\n","
STTTên
0T-shirt/top
1Trouser
2Pullover
3Dress
4Coat
5Sandal
6Shirt
7Sneaker
8Bag
9Ankle boot
"]},{"cell_type":"markdown","metadata":{"id":"SJqFEif2adfw"},"source":["### Import thư viện"]},{"cell_type":"code","metadata":{"id":"uk-sg_wJZB4M"},"source":["# Tensorflow: là thư viện mã nguồn mở được phát triển bởi Google, được sử dụng cho việc tính toán số học sử dụng đồ thị luồng dữ liệu.\n","# Numpy: là thư viện hỗ trợ cho việc tính toán các mảng nhiều chiều. Numpy cực kì hữu ích khi thực hiện các hàm liên quan đến Đại số tuyến tính.\n","import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"i4V2UgwSbQuC"},"source":["### Trực quan hóa dữ liệu"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":566},"id":"YLGAKnx0bXpP","executionInfo":{"status":"ok","timestamp":1637284939098,"user_tz":-420,"elapsed":1612,"user":{"displayName":"Anh Đức Lê","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggl2R3s4-MP-Ma4tbGGDacR2dirRQjQHyn5v6zLpg=s64","userId":"12415970068997449260"}},"outputId":"99dc96eb-8be8-4686-8c42-31333ac20ea4"},"source":["# Trước hết, chúng ta sử dụng keras.datasets để load dữ liệu để hiển thị một số thông tin về dataset.\n","# Tải dữ liệu thời trang\n","(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n","\n","# In hình dạng tập huấn luyện\n","# Lưu ý: có 60.000 dữ liệu huấn luyện có kích thước hình ảnh 28x28, 60.000 nhãn tập huấn.\n","print(\"x_train shape:\", x_train.shape)\n","print(\"y_train shape:\", y_train.shape)\n","\n","# In số lượng tập dữ liệu đào tạo và kiểm tra\n","print('Train set:', x_train.shape[0])\n","print('Test set:', x_test.shape[0])\n","\n","# Xác định các nhãn\n","fashion_mnist_labels = [\n"," \"T-shirt/top\", # index 0\n"," \"Trouser\", # index 1\n"," \"Pullover\", # index 2 \n"," \"Dress\", # index 3 \n"," \"Coat\", # index 4\n"," \"Sandal\", # index 5\n"," \"Shirt\", # index 6 \n"," \"Sneaker\", # index 7 \n"," \"Bag\", # index 8 \n"," \"Ankle boot\" # index 9\n","]\n","\n","# Chọn bất kỳ 1 số trong khoảng từ 0 đến 59,999\n","img_index = 5\n","\n","# y_train chứa các nhãn, từ 0 đến 9 (index 0 -> 9 tương ứng với mỗi loại fashion)\n","label_index = y_train[img_index]\n","\n","# In nhãn, ví dụ nhãn thứ 2 là Pullover (áo phông)\n","print(\"y = \" + str(label_index) + \" \" +(fashion_mnist_labels[label_index]))\n","\n","# Hiển thị một trong những hình ảnh từ tập dữ liệu đào tạo\n","plt.imshow(x_train[img_index])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","32768/29515 [=================================] - 0s 0us/step\n","40960/29515 [=========================================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","26427392/26421880 [==============================] - 0s 0us/step\n","26435584/26421880 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","16384/5148 [===============================================================================================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","4423680/4422102 [==============================] - 0s 0us/step\n","4431872/4422102 [==============================] - 0s 0us/step\n","x_train shape: (60000, 28, 28)\n","y_train shape: (60000,)\n","Train set: 60000\n","Test set: 10000\n","y = 2 Pullover\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":3},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"867sJwDiDZq3"},"source":["### Chuẩn hoá dữ liệu"]},{"cell_type":"markdown","metadata":{"id":"UnlUe8ZFTp1s"},"source":["Chuẩn hóa các dữ liệu để chúng có cùng tỷ lệ. Ở đây, `x_train`, `x_test` mang các giá trị nguyên từ 0 đến 255."]},{"cell_type":"code","metadata":{"id":"IS6nkCpmDfE6"},"source":["x_train = x_train.astype('float32') / 255\n","x_test = x_test.astype('float32') / 255"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FmjaqA5BD1to","executionInfo":{"status":"ok","timestamp":1637284947569,"user_tz":-420,"elapsed":5,"user":{"displayName":"Anh Đức Lê","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggl2R3s4-MP-Ma4tbGGDacR2dirRQjQHyn5v6zLpg=s64","userId":"12415970068997449260"}},"outputId":"584c8801-150f-41d2-de39-69402f8d6817"},"source":["print(\"Số lượng dữ liệu train: \" + str(len(x_train)))\n","print(\"Số lượng dữ liệu test: \" + str(len(x_test)))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Số lượng dữ liệu train: 60000\n","Số lượng dữ liệu test: 10000\n"]}]},{"cell_type":"markdown","metadata":{"id":"H3qr2hcFFGcA"},"source":["### Chia dữ liệu thành các tập dữ liệu\n"]},{"cell_type":"markdown","metadata":{"id":"UW4JyludTgc0"},"source":["* **Training data**: được sử dụng để đào tạo mô hình\n","* **Validation data**: được sử dụng để điều chỉnh các siêu tham số và đánh giá các mô hình\n","* **Test data**: được sử dụng để kiểm tra mô hình sau khi mô hình đã trải qua quá trình kiểm duyệt ban đầu bởi bộ xác nhận."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"obRBJcMsUO38","executionInfo":{"status":"ok","timestamp":1637284952165,"user_tz":-420,"elapsed":325,"user":{"displayName":"Anh Đức Lê","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggl2R3s4-MP-Ma4tbGGDacR2dirRQjQHyn5v6zLpg=s64","userId":"12415970068997449260"}},"outputId":"92592f72-e5f1-4285-a6fd-fb090bb13f0d"},"source":["# Chia nhỏ dữ liệu thành các tập huấn luyện\n","# Trích từ tập training data ra một tập con nhỏ và thực hiện việc đánh giá mô hình trên tập con nhỏ này. Tập con nhỏ được trích ra từ training set này được gọi là validation set\n","# Đặt 5000 vào validation set và giữ 55.000 còn lại cho training set\n","(x_train, x_valid) = x_train[5000:], x_train[:5000]\n","(y_train, y_valid) = y_train[5000:], y_train[:5000]\n","\n","# Định hình lại dữ liệu đầu vào từ (28, 28) thành (28, 28, 1)\n","w, h = 28, 28\n","x_train = x_train.reshape(x_train.shape[0], w, h, 1)\n","x_valid = x_valid.reshape(x_valid.shape[0], w, h, 1)\n","x_test = x_test.reshape(x_test.shape[0], w, h, 1)\n","\n","# One-hot encode the labels\n","# One-hot encoding là một quá trình mà các biến phân loại (label) được chuyển đổi thành một mẫu có thể cung cấp cho các thuật toán để thực hiện công việc tốt hơn khi mà dự đoán.\n","y_train = tf.keras.utils.to_categorical(y_train, 10)\n","y_valid = tf.keras.utils.to_categorical(y_valid, 10)\n","y_test = tf.keras.utils.to_categorical(y_test, 10)\n","\n","# In tập huấn luyện\n","print(\"x_train shape:\", x_train.shape)\n","print(\"y_train shape:\", y_train.shape)\n","\n","# In số lượng tập dữ liệu of training, validation, and test datasets\n","print('Train set:', x_train.shape[0])\n","print('Validation set:', x_valid.shape[0],)\n","print('Test set:', x_test.shape[0],)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["x_train shape: (55000, 28, 28, 1)\n","y_train shape: (55000, 10)\n","Train set: 55000\n","Validation set: 5000\n","Test set: 10000\n"]}]},{"cell_type":"markdown","metadata":{"id":"HUKGw7QF698K"},"source":["### Xây dựng mô hình"]},{"cell_type":"markdown","metadata":{"id":"LfO1WKWfYh1b"},"source":["Có 2 API để xác định 1 mô hình trong Keras\n","\n","* [Sequential model API](https://keras.io/models/sequential/)\n","* [Functional API](https://keras.io/models/model/)\n","\n","Ở bài này, bọn em sử dụng mô mình **Sequential model API** với một số API Keras sau:\n","\n","* [Conv2D()](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D/): Là convolution layer dùng để lấy feature từ image.\n","* [Pooling()](https://keras.io/layers/pooling/): Chứa các layer dùng trong mạng\n","* [Dropout()](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout): Hạn chế overfiting\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lJ_eCscHo8k8","executionInfo":{"status":"ok","timestamp":1637284958647,"user_tz":-420,"elapsed":1106,"user":{"displayName":"Anh Đức Lê","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggl2R3s4-MP-Ma4tbGGDacR2dirRQjQHyn5v6zLpg=s64","userId":"12415970068997449260"}},"outputId":"53deba30-c8ce-49e1-8aef-c844dfe7e9b7"},"source":["# Khởi tạo models Sequential()\n","model = tf.keras.Sequential()\n","\n","# Xác định hình dạng đầu vào trong lớp đầu tiên của neural network\n","# Tạo Convolutionnal Layers: Conv2D là convolution dùng để lấy feature từ ảnh với các tham số\n","# filters: số filter của convolution\n","# kernel_size: kích thước window search trên ảnh\n","# activation: chọn activation như linear, softmax, relu, tanh, sigmoid (relu là hàm trả về giá trị tích cực, nhưng không trả lại giá trị âm)\n","# padding: có thể là \"valid\" hoặc \"same\". Với same thì có nghĩa là padding =1.\n","model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(28,28,1)))\n","# Hàm MaxPooling2D hoặc AvergaPooling1D, 2D (lấy max , trung bình) với từng size.\n","# pool_size: kích thước ma trận để lấy max hay average\n","model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n","# Dropout: chống over-fitting\n","# Hiểu đơn giản là, trong mạng neural network, kỹ thuật dropout là việc chúng ta sẽ bỏ qua một vài unit trong suốt quá trình train trong mô hình, những unit bị bỏ qua được lựa chọn ngẫu nhiên.\n","model.add(tf.keras.layers.Dropout(0.3))\n","\n","model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))\n","model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n","model.add(tf.keras.layers.Dropout(0.3))\n","\n","# Flatten dùng để lát phằng layer để fully connection (ví dụ: shape: 28x28 qua layer này sẽ là 784x1)\n","model.add(tf.keras.layers.Flatten())\n","model.add(tf.keras.layers.Dense(256, activation='relu'))\n","model.add(tf.keras.layers.Dropout(0.5))\n","model.add(tf.keras.layers.Dense(10, activation='softmax'))\n","\n","# Bảng tóm tắt mô hình\n","model.summary()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type) Output Shape Param # \n","=================================================================\n"," conv2d (Conv2D) (None, 28, 28, 64) 320 \n"," \n"," max_pooling2d (MaxPooling2D (None, 14, 14, 64) 0 \n"," ) \n"," \n"," dropout (Dropout) (None, 14, 14, 64) 0 \n"," \n"," conv2d_1 (Conv2D) (None, 14, 14, 32) 8224 \n"," \n"," max_pooling2d_1 (MaxPooling (None, 7, 7, 32) 0 \n"," 2D) \n"," \n"," dropout_1 (Dropout) (None, 7, 7, 32) 0 \n"," \n"," flatten (Flatten) (None, 1568) 0 \n"," \n"," dense (Dense) (None, 256) 401664 \n"," \n"," dropout_2 (Dropout) (None, 256) 0 \n"," \n"," dense_1 (Dense) (None, 10) 2570 \n"," \n","=================================================================\n","Total params: 412,778\n","Trainable params: 412,778\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","metadata":{"id":"HyCYfF8wf6GL"},"source":["### Biên dịch mô hình"]},{"cell_type":"markdown","metadata":{"id":"pV7glI6AgUTV"},"source":["Cấu hình quy trình huấn luyện với **compile() API** trước khi huấn luyện mô hình."]},{"cell_type":"code","metadata":{"id":"A55OGQaPg4DS"},"source":["# categorical_crossentropy: Dùng trong classifier nhiều class\n","# adam optimizer: Thuật toán tối ưu hoá\n","# metrics: Thước đo để ta đánh giá accuracy của model\n","model.compile(\n"," loss='categorical_crossentropy',\n"," optimizer='adam',\n"," metrics=['accuracy']\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pqhOFnYSiYzS"},"source":["### Huấn luyện mô hô hình"]},{"cell_type":"markdown","metadata":{"id":"6zsyFA-1i3v0"},"source":["Huấn luyện mô hình với **fit() API**.\n","\n","Sử dụng [ModelCheckpoint API](https://keras.io/callbacks/#modelcheckpoint) để lưu mô hình sau mỗi epoch. Đặt `save_best_only = True` để chỉ lưu khi độ chính xác của validation set được cải thiện."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pjM8wJWmkUya","executionInfo":{"status":"ok","timestamp":1637287597190,"user_tz":-420,"elapsed":1337850,"user":{"displayName":"Anh Đức Lê","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggl2R3s4-MP-Ma4tbGGDacR2dirRQjQHyn5v6zLpg=s64","userId":"12415970068997449260"}},"outputId":"c56f1bef-780e-4a46-a768-d1b0e7b961e6"},"source":["from keras.callbacks import ModelCheckpoint\n","\n","checkpointer = ModelCheckpoint(\n"," filepath='model.weights.best.hdf5',\n"," verbose = 1,\n"," save_best_only = True\n",")\n","\n","# Batch_size: số lượng mẫu mà Mini-batch GD sử dụng cho mỗi lần cập nhật trọng số.\n","# Ta có tập huấn luyện gồm 55.000 hình ảnh, chọn batch-size là 100 images có nghĩa là mỗi lần cập nhật trọng số, ta dùng 100 images. Lúc đó ta mất 55.000/100 = 550 iterations (số lần lặp) để duyệt qua hết tập huấn luyện (hoàn thành 1 epochs). Có nghĩa là khi dữ liệu quá lớn, chúng ta không thể đưa cả tập data vào train được, ta phải chia nhỏ data ra thành nhiều batch nhỏ hơn.\n","# batch_size (default = 1): Batch size ảnh hưởng đến chất lượng học của mô hình, batch size càng lớn thì mô hình sẽ càng học được tốt hơn.\n","# Epoch là số lần duyệt qua hết số lượng mẫu trong tập huấn luyện.\n","model.fit(\n"," x_train,\n"," y_train,\n"," batch_size = 100,\n"," epochs = 20,\n"," validation_data=(x_valid, y_valid),\n"," callbacks=[checkpointer]\n",")"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2271 - accuracy: 0.9153\n","Epoch 00001: val_loss improved from inf to 0.21297, saving model to model.weights.best.hdf5\n","550/550 [==============================] - 72s 132ms/step - loss: 0.2271 - accuracy: 0.9153 - val_loss: 0.2130 - val_accuracy: 0.9234\n","Epoch 2/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2235 - accuracy: 0.9174\n","Epoch 00002: val_loss improved from 0.21297 to 0.20920, saving model to model.weights.best.hdf5\n","550/550 [==============================] - 68s 123ms/step - loss: 0.2235 - accuracy: 0.9174 - val_loss: 0.2092 - val_accuracy: 0.9250\n","Epoch 3/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2176 - accuracy: 0.9184\n","Epoch 00003: val_loss improved from 0.20920 to 0.20476, saving model to model.weights.best.hdf5\n","550/550 [==============================] - 67s 122ms/step - loss: 0.2176 - accuracy: 0.9184 - val_loss: 0.2048 - val_accuracy: 0.9240\n","Epoch 4/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2198 - accuracy: 0.9192\n","Epoch 00004: val_loss improved from 0.20476 to 0.20111, saving model to model.weights.best.hdf5\n","550/550 [==============================] - 66s 121ms/step - loss: 0.2198 - accuracy: 0.9192 - val_loss: 0.2011 - val_accuracy: 0.9254\n","Epoch 5/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2121 - accuracy: 0.9205\n","Epoch 00005: val_loss did not improve from 0.20111\n","550/550 [==============================] - 67s 121ms/step - loss: 0.2121 - accuracy: 0.9205 - val_loss: 0.2039 - val_accuracy: 0.9240\n","Epoch 6/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2128 - accuracy: 0.9196\n","Epoch 00006: val_loss improved from 0.20111 to 0.19610, saving model to model.weights.best.hdf5\n","550/550 [==============================] - 66s 121ms/step - loss: 0.2128 - accuracy: 0.9196 - val_loss: 0.1961 - val_accuracy: 0.9278\n","Epoch 7/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2096 - accuracy: 0.9213\n","Epoch 00007: val_loss did not improve from 0.19610\n","550/550 [==============================] - 67s 121ms/step - loss: 0.2096 - accuracy: 0.9213 - val_loss: 0.1985 - val_accuracy: 0.9242\n","Epoch 8/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2052 - accuracy: 0.9230\n","Epoch 00008: val_loss did not improve from 0.19610\n","550/550 [==============================] - 66s 121ms/step - loss: 0.2052 - accuracy: 0.9230 - val_loss: 0.1984 - val_accuracy: 0.9258\n","Epoch 9/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2044 - accuracy: 0.9223\n","Epoch 00009: val_loss did not improve from 0.19610\n","550/550 [==============================] - 67s 121ms/step - loss: 0.2044 - accuracy: 0.9223 - val_loss: 0.1978 - val_accuracy: 0.9286\n","Epoch 10/20\n","550/550 [==============================] - ETA: 0s - loss: 0.2002 - accuracy: 0.9237\n","Epoch 00010: val_loss improved from 0.19610 to 0.19193, saving model to model.weights.best.hdf5\n","550/550 [==============================] - 67s 121ms/step - loss: 0.2002 - accuracy: 0.9237 - val_loss: 0.1919 - val_accuracy: 0.9272\n","Epoch 11/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1998 - accuracy: 0.9255\n","Epoch 00011: val_loss did not improve from 0.19193\n","550/550 [==============================] - 67s 122ms/step - loss: 0.1998 - accuracy: 0.9255 - val_loss: 0.2020 - val_accuracy: 0.9252\n","Epoch 12/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1971 - accuracy: 0.9265\n","Epoch 00012: val_loss did not improve from 0.19193\n","550/550 [==============================] - 66s 121ms/step - loss: 0.1971 - accuracy: 0.9265 - val_loss: 0.2005 - val_accuracy: 0.9258\n","Epoch 13/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1935 - accuracy: 0.9279\n","Epoch 00013: val_loss improved from 0.19193 to 0.19004, saving model to model.weights.best.hdf5\n","550/550 [==============================] - 66s 119ms/step - loss: 0.1935 - accuracy: 0.9279 - val_loss: 0.1900 - val_accuracy: 0.9286\n","Epoch 14/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1945 - accuracy: 0.9276\n","Epoch 00014: val_loss did not improve from 0.19004\n","550/550 [==============================] - 66s 121ms/step - loss: 0.1945 - accuracy: 0.9276 - val_loss: 0.2021 - val_accuracy: 0.9270\n","Epoch 15/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1957 - accuracy: 0.9261\n","Epoch 00015: val_loss did not improve from 0.19004\n","550/550 [==============================] - 67s 121ms/step - loss: 0.1957 - accuracy: 0.9261 - val_loss: 0.1926 - val_accuracy: 0.9306\n","Epoch 16/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1885 - accuracy: 0.9290\n","Epoch 00016: val_loss did not improve from 0.19004\n","550/550 [==============================] - 67s 122ms/step - loss: 0.1885 - accuracy: 0.9290 - val_loss: 0.1968 - val_accuracy: 0.9248\n","Epoch 17/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1902 - accuracy: 0.9287\n","Epoch 00017: val_loss did not improve from 0.19004\n","550/550 [==============================] - 67s 122ms/step - loss: 0.1902 - accuracy: 0.9287 - val_loss: 0.1949 - val_accuracy: 0.9252\n","Epoch 18/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1893 - accuracy: 0.9295\n","Epoch 00018: val_loss did not improve from 0.19004\n","550/550 [==============================] - 67s 121ms/step - loss: 0.1893 - accuracy: 0.9295 - val_loss: 0.1938 - val_accuracy: 0.9322\n","Epoch 19/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1856 - accuracy: 0.9300\n","Epoch 00019: val_loss did not improve from 0.19004\n","550/550 [==============================] - 66s 121ms/step - loss: 0.1856 - accuracy: 0.9300 - val_loss: 0.1924 - val_accuracy: 0.9292\n","Epoch 20/20\n","550/550 [==============================] - ETA: 0s - loss: 0.1835 - accuracy: 0.9309\n","Epoch 00020: val_loss did not improve from 0.19004\n","550/550 [==============================] - 66s 121ms/step - loss: 0.1835 - accuracy: 0.9309 - val_loss: 0.2035 - val_accuracy: 0.9272\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","metadata":{"id":"Om6kdZwwn8nB"},"source":["### Tải mô hình với validation tốt nhất"]},{"cell_type":"code","metadata":{"id":"a_RSfZ0hoEBN"},"source":["model.load_weights('model.weights.best.hdf5')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2-I04n9pNh28"},"source":["### Dự đoán"]},{"cell_type":"markdown","metadata":{"id":"VdK-jne-Nl1d"},"source":["In ra 15 hình ảnh từ tập dữ liệu test set và đặt tiêu đề với dự đoán. Nếu dự đoán đúng (giống nhãn), thì tiêu đề sẽ có màu xanh và ngược lại là màu đỏ."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":594},"id":"MGj8YN14PEX5","executionInfo":{"status":"ok","timestamp":1637287769089,"user_tz":-420,"elapsed":4210,"user":{"displayName":"Anh Đức Lê","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggl2R3s4-MP-Ma4tbGGDacR2dirRQjQHyn5v6zLpg=s64","userId":"12415970068997449260"}},"outputId":"87c5a3d6-bd88-42bc-fd72-b78226794240"},"source":["# Kiểm tra mô hình bằng cách sử dụng model.predict dữ liệu thử nghiệm\n","y_check = model.predict(x_test)\n","\n","# Lấy ngẫu nhiên 15 hình ảnh từ tập test để dự đoán\n","figure = plt.figure(figsize=(20,10)) # Đặt kích thức hình ảnh\n","for i, index in enumerate(np.random.choice(x_test.shape[0], size=15, replace=False)): # ramdom 15 ảnh từ tập dữ liệu test\n"," ax = figure.add_subplot(3, 5, i + 1, xticks=[], yticks=[])\n"," \n"," # Hiển thị từng hình ảnh\n"," ax.imshow(np.squeeze(x_test[index]))\n"," predict_index = np.argmax(y_check[index])\n"," true_index = np.argmax(y_test[index])\n","\n"," # Đặt tiêu đề cho mỗi hình ảnh\n"," ax.set_title(\"{} ({})\".format(\n"," fashion_mnist_labels[predict_index],\n"," fashion_mnist_labels[true_index]),\n"," color=(\"green\" if predict_index == true_index else \"red\")\n"," )"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"GWWght_IpC3o"},"source":["### Kiểm tra độ chính xác"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qrvbG5QfpNHT","executionInfo":{"status":"ok","timestamp":1637287779921,"user_tz":-420,"elapsed":3439,"user":{"displayName":"Anh Đức Lê","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggl2R3s4-MP-Ma4tbGGDacR2dirRQjQHyn5v6zLpg=s64","userId":"12415970068997449260"}},"outputId":"4955c81d-95b1-4fa9-dbf0-6e46bba783de"},"source":["# Đánh giá mô hình trên test set\n","score = model.evaluate(x_test, y_test, verbose=0)\n","\n","# \bKiểm tra độ chính xác\n","print(\"Test loss:\", score[0])\n","print(\"Test accuracy:\", score[1])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Test loss: 0.20774540305137634\n","Test accuracy: 0.9258999824523926\n"]}]},{"cell_type":"markdown","metadata":{"id":"WwIQCagqYp60"},"source":["Đến thời điểm này, chúng ta thấy rằng độ accuracy của mô hình, khi đánh giá bằng tập kiểm thử, hơi thấp hơn so với số liệu trong quá trình huấn luyện. Khoảng cách giữa hai độ accuracy khi huấn luyện và khi kiểm thử thể hiện sự overfitting. Overfitting xảy ra khi một mô hình ML hoạt động kém hơn khi được cung cấp các đầu vào mới, mà mô hình chưa từng thấy trước đây trong quá trình đào tạo."]},{"cell_type":"markdown","metadata":{"id":"3l46UW9KSa1y"},"source":["### Kết luận"]},{"cell_type":"markdown","metadata":{"id":"rqnbRU65SnAT"},"source":["Huấn luyện thành công Convolutional Neural Network (CNN) để phân loại Fashion-MNIST với độ chính xác khoảng 92% 🎉"]}]}