我是张量流编程的新手。我想在下面的程序中绘制训练准确性、训练损失、验证准确性和验证损失。我在google colab中使用tensorflow版本1.x。代码片段如下。
# hyperparameters
n_neurons = 128
learning_rate = 0.001
batch_size = 128
n_epochs = 5
# parameters
n_steps = 32
n_inputs = 32
n_outputs = 10
# build a rnn model
X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.int32, [None])
cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)
output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
logits = tf.layers.dense(state, n_outputs)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
loss = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
prediction = tf.nn.in_top_k(logits, y, 1)
accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))
# input data
x_test = x_test.reshape([-1, …