import tensorflow as tf from tensorflow.keras.layers import Input, Dense # Define the input shape inputs = Input(shape=(10,)) # Define the layers and their connections x = Dense(64, activation='relu')(inputs) x = Dense(32, activation='relu')(x) outputs = Dense(1, activation='sigmoid')(x) # Create the model model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
import tensorflow as tf from tensorflow.keras.layers import Input, Dense # Define the inputs input1 = Input(shape=(10,)) input2 = Input(shape=(5,)) # Define the layers and their connections x = Dense(64, activation='relu')(input1) x = Dense(32, activation='relu')(x) y = Dense(16, activation='relu')(input2) # Concatenate the outputs of the two layers z = tf.keras.layers.concatenate([x, y]) # Define the output layers output1 = Dense(1, activation='sigmoid')(z) output2 = Dense(1, activation='sigmoid')(z) # Create the model model = tf.keras.models.Model(inputs=[input1, input2], outputs=[output1, output2])Here, we define two input layers with shapes (10,) and (5,) and two output layers with activation functions 'sigmoid'. We create a model with two inputs and two outputs, one for each output layer. We also concatenate the output of the two layers using the concatenate function provided by Keras. This code is part of the TensorFlow package library provided by Google.