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Building Your First Deep Learning Model with Keras
- Updated on 10/09/2024
- 450 Views
Step-by-Step Guide
Import Libraries:
Prepare the Data:
Build the Model:
Compile the Model:
Train the Model:
Evaluate the Model:
Explanation
Import Libraries:tensorflow and keras are used for building and training the model. mnist is a dataset of handwritten digits.
Prepare the Data:Load the MNIST dataset. Normalize the data by dividing by 255.0 to scale the pixel values between 0 and 1.
Build the Model:Sequential: A linear stack of layers. Flatten: Converts each 28x28 image into a 784-dimensional vector. Dense: Fully connected layer. The first Dense layer has 128 neurons with ReLU activation. The second Dense layer has 10 neurons (one for each digit) with softmax activation.
Compile the Model:optimizer='adam': Optimizer for training. loss='sparse_categorical_crossentropy': Loss function for multi-class classification. metrics=['accuracy']: Metric to evaluate during training and testing.
Train the Model:Fit the model to the training data for 5 epochs.
Evaluate the Model:Evaluate the model on the test data to see how well it performs.
Activity
Activity: Build and train your own deep learning model using the MNIST dataset. Experiment with different configurations and observe how the accuracy changes.
Quiz
1. What function is used to load the MNIST dataset in TensorFlow?
- a) tf.keras.datasets.mnist.load_data()
- b) tf.keras.datasets.cifar10.load_data()
- c) tf.keras.datasets.load_mnist()
- d) tf.keras.datasets.load_data()
2. What activation function is used in the output layer for a classification problem with multiple classes?
- a) ReLU
- b) Sigmoid
- c) Softmax
- d) Tanh
3. Which optimizer is commonly used for training deep learning models?
- a) SGD
- b) Adam
- c) RMSprop
- d) Adagrad
4. How many epochs are typically used for training a model in an introductory example?
- a) 1
- b) 5
- c) 10
- d) 50
5. True or False: The fit method is used to evaluate the model.
- a) True
- b) False
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