import tensorflow as tf from tensorflow import keras model = keras.Sequential([ keras.layers.Dense(32, activation='relu', input_shape=(784,)), keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=5, batch_size=32)
import tensorflow as tf from tensorflow import keras input1 = keras.Input(shape=(64,)) input2 = keras.Input(shape=(64,)) x1 = keras.layers.Dense(16, activation='relu')(input1) x2 = keras.layers.Dense(16, activation='relu')(input2) concat = keras.layers.concatenate([x1, x2]) output = keras.layers.Dense(1, activation='sigmoid')(concat) model = keras.Model(inputs=[input1, input2], outputs=output) model.compile(optimizer='adam', loss=lambda y_true, y_pred: tf.math.abs(y_true - y_pred)) model.fit([x_train1, x_train2], y_train, epochs=10, batch_size=32)In this example, the model has two input layers with 64 neurons each, two hidden layers with 16 neurons and ReLU activation function, and one output layer with 1 neuron and sigmoid activation function (used for binary classification). The model is constructed using the functional API of Keras, where the inputs and outputs are explicitly defined. The model is first compiled with an optimizer algorithm and a custom loss function that computes the absolute difference between the true labels and predicted labels. Then it is trained on two sets of input data x_train1 and x_train2, and labels y_train, for 10 epochs with a batch size of 32. Package library: TensorFlow.