simplay / tf-demo

Steps

• Normalize the Dataset
• Build the Model
• Train the model

Prepare the Datasets

train_images = train_images / 255.0
test_images = test_images / 255.0

Define the Structure

model = keras.Sequential([
  keras.layers.Flatten(input_shape=(28, 28)),
  keras.layers.Dense(128, activation='relu'),
  keras.layers.Dense(10, activation='softmax')
])

1. Flatten transforms the 2D images (28px x 28px) to a a 1D Array (of size 28 * 28)
2. 1st layer has 128 nodes (relu)
3. 2nd layer has 10 nodes (softmax)

Configure the Model

model.compile(
  optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy']
)

• optimizer: Define training procedure - how the model is updated
• loss: minimization function used in optimization
• metrics: used to monitor training and testing steps

Train the Model

model.fit(train_images, train_labels, epochs=10)

• Feeds training data to model. The model learns to associate images with labels
• epoch: iteration over the entire input data.

Evaluate the Accuracy

Compare how models pferoms on test dataset

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)

Make Predictions

Apply trained model on new datasets

predictions = model.predict(test_images)