# Solution is available in the other "solution.py" tab import tensorflow as tf output = None hidden_layer_weights = [ [0.1, 0.2, 0.4], [0.4, 0.6, 0.6], [0.5, 0.9, 0.1], [0.8, 0.2, 0.8]] out_weights = [ [0.1, 0.6], [0.2, 0.1], [0.7, 0.9]] # Weights and biases weights = [ tf.Variable(hidden_layer_weights), tf.Variable(out_weights)] biases = [ tf.Variable(tf.zeros(3)), tf.Variable(tf.zeros(2))] # Input features = tf.Variable([[1.0, 2.0, 3.0, 4.0], [-1.0, -2.0, -3.0, -4.0], [11.0, 12.0, 13.0, 14.0]]) # TODO: Create Model hidden_layer = tf.add(tf.matmul(features, weights[0]), biases[0]) hidden_layer = tf.nn.relu(hidden_layer) logits = tf.add(tf.matmul(hidden_layer, weights[1]), biases[1]) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) print(sess.run(logits))
[[ 5.11 8.440001] [ 0. 0. ] [24.010002 38.239998]]
通过类似的方法,可以实现更多层神经网络。