model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss=tf.keras.losses.categorical_crossentropy, metrics=['accuracy'])
时间: 2023-04-08 19:02:08 浏览: 227
这是一个关于 TensorFlow 模型编译的问题,我可以回答。这段代码是在编译模型时指定了优化器、损失函数和评估指标。其中,优化器使用 Adam 算法,学习率为 0.001;损失函数使用分类交叉熵;评估指标为准确率。
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)
#使用tensorflow库实现GCN模型,将邻接矩阵和特征矩阵输入模型中进行嵌入。 import tensorflow as tf # 定义GCN模型 class GCN(tf.keras.Model): def __init__(self, input_dim, hidden_dim, output_dim): super(GCN, self).__init__() self.dense1 = tf.keras.layers.Dense(hidden_dim, activation="relu") self.dense2 = tf.keras.layers.Dense(output_dim) self.dropout = tf.keras.layers.Dropout(0.5) def call(self, adj_matrix, feature_matrix, training=True): x = tf.matmul(adj_matrix, feature_matrix) x = self.dense1(x) x = self.dropout(x, training=training) x = self.dense2(x) return x # 定义GCN模型的超参数 input_dim = len(feature_names) hidden_dim = 64 output_dim = 32 # 创建GCN模型 model = GCN(input_dim, hidden_dim, output_dim) # 编译模型 model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), loss=tf.keras.losses.MeanSquaredError(), metrics=[tf.keras.metrics.MeanSquaredError()]) # 训练模型 model.fit(x=(adj_matrix, feature_matrix), y=feature_matrix, epochs=100, batch_size=32)报错:ValueError: Models passed to `fit` can only have `training` and the first argument in `call` as positional arguments, found: ['feature_matrix'].
这个错误是因为在调用`model.fit()`方法时传递的输入数据参数是`(adj_matrix, feature_matrix)`,但是在`model.call()`方法中定义的参数是`(adj_matrix, feature_matrix, training=True)`,多了一个`training=True`的默认参数。因此,需要修改`model.call()`方法的定义,去掉默认参数`training=True`,如下所示:
```python
# 定义GCN模型
class GCN(tf.keras.Model):
def __init__(self, input_dim, hidden_dim, output_dim):
super(GCN, self).__init__()
self.dense1 = tf.keras.layers.Dense(hidden_dim, activation="relu")
self.dense2 = tf.keras.layers.Dense(output_dim)
self.dropout = tf.keras.layers.Dropout(0.5)
def call(self, adj_matrix, feature_matrix):
x = tf.matmul(adj_matrix, feature_matrix)
x = self.dense1(x)
x = self.dropout(x)
x = self.dense2(x)
return x
# 定义GCN模型的超参数
input_dim = len(feature_names)
hidden_dim = 64
output_dim = 32
# 创建GCN模型
model = GCN(input_dim, hidden_dim, output_dim)
# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),
loss=tf.keras.losses.MeanSquaredError(),
metrics=[tf.keras.metrics.MeanSquaredError()])
# 训练模型
model.fit(x=(adj_matrix, feature_matrix), y=feature_matrix, epochs=100, batch_size=32)
```
这样修改后,就可以成功训练模型了。