||“`ok|
import tensorflow as tf

Image: www.tradethetechnicals.com

model = tf.keras.Sequential([
tf.keras.layers.Dense(units=10, activation=’relu’, input_shape=(784,)),
tf.keras.layers.Dense(units=10, activation=’relu’),
tf.keras.layers.Dense(units=10, activation=’softmax’)
])

Compile the model with loss and optimizer.

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

Create the input data.

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

Reshape the input data.

x_train = x_train.reshape(-1, 784).astype(‘float32’) / 255
x_test = x_test.reshape(-1, 784).astype(‘float32’) / 255

Train the model.

model.fit(x_train, y_train, epochs=10)

Evaluate the model.

model.evaluate(x_test, y_test)

Save the model.

model.save(‘my_model.h5’)This code is written in Python and uses the TensorFlow library to create and train a neural network model for classifying handwritten digits from the MNIST dataset. Here’s a breakdown of the code:

1. Importing TensorFlow: The code begins by importing the TensorFlow library using the following line:

import tensorflow as tf

2. Creating a Model: A sequential model is created using the tf.keras.Sequential class. This model consists of three layers:
   • The first layer is a dense layer with 10 units (neurons) and a ReLU activation function. It takes an input shape of (784,).
   • The second layer is also a dense layer with 10 units and a ReLU activation function.
   • The third layer is another dense layer with 10 units and a softmax activation function. This layer is used for classification tasks with multiple classes.

model = tf.keras.Sequential([
  tf.keras.layers.Dense(units=10, activation='relu', input_shape=(784,)),
  tf.keras.layers.Dense(units=10, activation='relu'),
  tf.keras.layers.Dense(units=10, activation='softmax')
])

3. Compiling the Model: The model is compiled with the following configuration:
   • Optimizer: Adam, an adaptive learning rate optimization algorithm.
   • Loss: Sparse categorical crossentropy, suitable for multi-class classification problems.
   • Metrics: Accuracy, which measures the percentage of correct predictions.

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

4. Preparing the Input Data: The MNIST dataset is loaded using tf.keras.datasets.mnist.load_data(). The dataset is split into training and testing sets. The input data is reshaped to have a shape of (784,) to match the model’s input shape and normalized to values between 0 and 1.

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Reshape the input data.
x_train = x_train.reshape(-1, 784).astype('float32') / 255
x_test = x_test.reshape(-1, 784).astype('float32') / 255

5. Training the Model: The model is trained on the training data using the fit() method. The training process runs for 10 epochs.

model.fit(x_train, y_train, epochs=10)

6. Evaluating the Model: The model’s performance is evaluated on the test data using the evaluate() method. This returns the loss and accuracy metrics.

model.evaluate(x_test, y_test)

7. Saving the Model: The trained model is saved to a file named “my_model.h5” using the save() method. This allows the model to be loaded and reused later.

model.save('my_model.h5')

Overall, this code demonstrates the process of creating, compiling, training, evaluating, and saving a neural network model for handwritten digit classification using TensorFlow.

Image: www.investopedia.com

Options Trading Strategy Pdf

Image: lessonlistschulz.z19.web.core.windows.net