My goal is to classify products pictures into categories such as dress, sandals, etc.
I am using the MNIST fashion dataset, following this official tutorial word-per-word: https://www.tensorflow.org/tutorials/keras/basic_classification so my code is 100% identical to what can be read there:
# TensorFlow and tf.keras import tensorflow as tf from tensorflow import keras # Helper libraries import numpy as np import matplotlib.pyplot as plt print(tf.__version__) fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(10, activation=tf.nn.softmax) ]) model.compile(optimizer=tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, train_labels, epochs=5) test_loss, test_acc = model.evaluate(test_images, test_labels) print('Test accuracy:', test_acc)
Problem: The resulting accuracy is always around 0.1, much lower than the tutorial's example output of 0.876.
I am obviously doing something wrong, but I can't figure out what. How to improve accuracy to something reasonable?
$ python classify-products.py 1.10.1 Epoch 1/5 2018-09-18 13:33:46.971437: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 60000/60000 [==============================] - 3s 47us/step - loss: 13.0161 - acc: 0.1924 Epoch 2/5 60000/60000 [==============================] - 3s 46us/step - loss: 12.8998 - acc: 0.1997 Epoch 3/5 60000/60000 [==============================] - 3s 46us/step - loss: 13.3386 - acc: 0.1724 Epoch 4/5 60000/60000 [==============================] - 3s 47us/step - loss: 12.9031 - acc: 0.1995 Epoch 5/5 60000/60000 [==============================] - 3s 47us/step - loss: 13.6666 - acc: 0.1521 10000/10000 [==============================] - 0s 26us/step ('Test accuracy:', 0.1005)
Switching to 20 epochs does not improve accuracy.