!pip install pydot graphviz tensorflow

import tensorflow as tf from tensorflow.keras.utils import plot_model

# define the input layer input_layer = tf.keras.layers.Input(shape=(784,)) # define the hidden layer hidden_layer = tf.keras.layers.Dense(units=128, activation='relu')(input_layer) # define the output layer output_layer = tf.keras.layers.Dense(units=10, activation='softmax')(hidden_layer)

# create the model model = tf.keras.Model(inputs=input_layer, outputs=output_layer)

# compile the model model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

plot_model(model,show_shapes=True, show_layer_names=True)

# Load the MNIST dataset (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() # Reshape the input data to 784 x_train = x_train.reshape(-1, 784) / 255.0 x_test = x_test.reshape(-1, 784) / 255.0 # Convert the labels to one-hot encoding y_train = tf.keras.utils.to_categorical(y_train) y_test = tf.keras.utils.to_categorical(y_test)

# Train the model model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

import numpy as np # Use the model to make predictions on test data predictions = model.predict(x_test[:10]) # Get the predicted class labels for the first 10 images predicted_labels = np.argmax(predictions, axis=1) # Get the actual class labels for the first 10 images actual_labels = np.argmax(y_test[:10], axis=1) # Print the predicted and actual labels for the first 10 images for i in range(10): print("Image %d: Predicted label=%d, Actual label=%d" % (i+1, predicted_labels[i], actual_labels[i]))