scores = model.evaluate(X_test,y_test, verbose=1) print(model.metrics_names) print('test loss',scores[0]) print('test accuracy',scores[1])请你优化一下这段代码

这段代码已经比较简洁和清晰，但是可以进行一些小的优化：

1. 可以将模型评估阶段的 verbose 参数设置为 0，即不输出任何信息，以加快测试速度。

2. 可以使用 f-string 格式化输出信息，以使代码更加简洁易懂。

3. 可以将评估结果存储到一个字典中，以便在之后的程序中使用。

下面是优化后的代码：

eval_results = model.evaluate(X_test, y_test, verbose=0)
eval_dict = {metric_name: eval_result for metric_name, eval_result in zip(model.metrics_names, eval_results)}
print(f'Test loss: {eval_dict["loss"]:.4f}')
print(f'Test accuracy: {eval_dict["accuracy"]:.4f}')

相关问题

帮我找出下面代码的错误loss_fn = tf.keras.losses.MeanSquaredError() metrics = [tf.k optimizer = tf.keras.optimizers.Adam(learning_rate=0.01) eras.metrics.MeanAbsoluteError(), tf.keras.metrics.RootMeanSquaredError()] model = GCNModel(hidden_dim=64, output_dim=32) model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics) history=model.fit((train_features,train_adj),train_labels,epochs=50,validation_data=((val_features, val_adj), val_labels)) test_scores = model.evaluate((test_features, test_adj), test_labels, verbose=0)

这段代码的错误在于第三行中的"tf.k"，应该改为"tf.keras"。正确的是：

loss_fn = tf.keras.losses.MeanSquaredError()
metrics = [tf.keras.metrics.MeanAbsoluteError(), tf.keras.metrics.RootMeanSquaredError()]
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
model = GCNModel(hidden_dim=64, output_dim=32)
model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)
history=model.fit((train_features,train_adj),train_labels,epochs=50,validation_data=((val_features, val_adj), val_labels))
test_scores = model.evaluate((test_features, test_adj), test_labels, verbose=0)

找出这段代码错误import tensorflow as tf from tensorflow.keras import layers class GCNModel(tf.keras.Model): def __init__(self, hidden_dim, output_dim): super(GCNModel, self).__init__() self.gc1 = GraphConvolution(hidden_dim) self.gc2 = GraphConvolution(output_dim) self.relu = layers.ReLU() self.dropout = layers.Dropout(0.5) self.dense = layers.Dense(1) def call(self, inputs): x, adj = inputs x = self.gc1(x, adj) x = self.relu(x) x = self.dropout(x) x = self.gc2(x, adj) x = self.dense(tf.reduce_mean(x, axis=1)) return x loss_fn = tf.keras.losses.MeanSquaredError() metrics = [tf.keras.metrics.MeanAbsoluteError(), tf.keras.metrics.RootMeanSquaredError()] optimizer = tf.keras.optimizers.Adam(learning_rate=0.01) model = GCNModel(hidden_dim=64, output_dim=32) model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics) history=model.fit((train_features,train_adj),train_labels,epochs=50,validation_data=((val_features, val_adj), val_labels)) test_scores = model.evaluate((test_features, test_adj), test_labels, verbose=0)

这段代码的错误是缺少GraphConvolution类的定义和导入。需要在代码开头添加类的定义和导入。如果已经定义了GraphConvolution类，则可能是没有导入GraphConvolution类所在的模块。