valueerror 尝试将值（类型模型）转换为张量
valuerror attemp to convert a value (type model) to a tensor
我正在尝试使用 tensorflow 2 训练模型。
我收到错误消息：
ValueError: Attempt to convert a value (<tensorflow.python.keras.engine.training.Model object at 0x7f1ab822ecc0>) with an unsupported type (<class 'tensorflow.python.keras.engine.training.Model'>) to a Tensor.
当我尝试调用
return loss_object(y_true=y, y_pred=ypred)
在 loss 函数中。
ypred的类型是
<class'tensorflow.python.keras.engine.training.Model'>
虽然它应该是张量。
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
df = pd.DataFrame({'A': np.array([100, 105.4, 108.3, 111.1, 113, 114.7]),
'B': np.array([11, 11.8, 12.3, 12.8, 13.1,13.6]),
'C': np.array([55, 56.3, 57, 58, 59.5, 60.4]),
'Target': np.array([4000, 4200.34, 4700, 5300, 5800, 6400])})
def data():
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:, :3].values,
df.iloc[:, 3].values,
test_size=0.2,
random_state=134)
return X_train, X_test, y_train, y_test
X_train, X_test, y_train, y_test = data()
features = {'A': X_train[:, 0],
'B': X_train[:, 1],
'C': X_train[:, 2]}
labels = y_train
batch_size = 1
def train_input_fn(features, labels, batch_size):
train_dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
train_dataset = train_dataset.shuffle(1000).repeat().batch(batch_size)
return train_dataset
def pack_features_vector(features, labels):
'''Pack the features into a single array'''
features = tf.stack(list(features.values()), axis=1)
return features, labels
train_dataset = train_input_fn(features, labels, batch_size).map(pack_features_vector)
class Model():
def __init__(self):
pass
def build_model(self, features):
inputs = tf.keras.Input(shape=(features.shape[1],))
x = tf.keras.layers.Dense(2, activation='relu')(inputs)
preds = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs=inputs, outputs=preds)
return model
def loss(self, loss_object, X, y):
ypred = self.build_model(X)
print(type(ypred))
print(ypred)
return loss_object(y_true=y, y_pred=ypred)
def grad(self, loss_object, X, y):
with tf.GradientTape() as tape:
loss_value = self.loss(loss_object, X, y)
return loss_value, tape.gradient(loss_value, self.build_model(X).trainable_variables)
def train(self, X, y, optimizer, loss_object):
loss_value, grads = self.grad(loss_object, X, y)
optimizer.apply_gradients(zip(grads, self.build_model(X).trainable_variables))
learning_rate = 0.001
optimizer=tf.optimizers.RMSprop(learning_rate)
loss_object=tf.keras.losses.mean_squared_error
epochs = 1
for epoch in range(epochs):
epoch_loss_avg = tf.keras.metrics.Mean()
epoch_acc = tf.keras.metrics.MeanSquaredError()
for X, y in train_dataset:
Model().train(X, y, optimizer, loss_object)
如果我不使用 class 和 运行：
inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(2, activation='relu')(inputs)
preds = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs=inputs, outputs=preds)
for x, y in train_dataset:
ypred = model(x)
print(type(ypred))
loss_object(y, ypred)
运行没问题！
model(x)的类型是<class 'tensorflow.python.framework.ops.EagerTensor'>
但是在class代码中，self.build_model(X)的类型是model。
在方法中将第一行从 ypred = self.build_model(X) 更改为 ypred = self.build_model()(X)
使用您的数据设置 "works" 的另一种方法：
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
df = pd.DataFrame({'A': np.array([100, 105.4, 108.3, 111.1, 113, 114.7]),
'B': np.array([11, 11.8, 12.3, 12.8, 13.1,13.6]),
'C': np.array([55, 56.3, 57, 58, 59.5, 60.4]),
'Target': np.array([4000, 4200.34, 4700, 5300, 5800, 6400])})
def data():
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:, :3].values,
df.iloc[:, 3].values,
test_size=0.2,
random_state=134)
return X_train, X_test, y_train, y_test
X_train, X_test, y_train, y_test = data()
features = {'A': X_train[:, 0],
'B': X_train[:, 1],
'C': X_train[:, 2]}
labels = y_train
batch_size = 1
def train_input_fn(features, labels, batch_size):
train_dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
train_dataset = train_dataset.shuffle(1000).repeat().batch(batch_size)
return train_dataset
def pack_features_vector(features, labels):
'''Pack the features into a single array'''
features = tf.stack(list(features.values()), axis=1)
return features, labels
train_dataset = train_input_fn(features, labels, batch_size).map(pack_features_vector)
class Model(tf.keras.Model):
def __init__(self):
super(Model,self).__init__()
self.l1= tf.keras.layers.Dense(2, activation='relu')
self.out = tf.keras.layers.Dense(1)
def __call__(self,x):
x=self.l1(x)
return self.out(x)
learning_rate = 1
optimizer=tf.optimizers.RMSprop(learning_rate)
loss_object=tf.keras.losses.mean_squared_error
model = Model()
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
for x,y in train_dataset:
with tf.GradientTape() as tape:
y_ = model(x)
loss = loss_object(y, y_)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
print("loss",train_loss(loss),"accuracy",train_accuracy(y,y_))
我正在尝试使用 tensorflow 2 训练模型。
我收到错误消息：
ValueError: Attempt to convert a value (<tensorflow.python.keras.engine.training.Model object at 0x7f1ab822ecc0>) with an unsupported type (<class 'tensorflow.python.keras.engine.training.Model'>) to a Tensor.
当我尝试调用
return loss_object(y_true=y, y_pred=ypred)
在 loss 函数中。
ypred的类型是
<class'tensorflow.python.keras.engine.training.Model'>
虽然它应该是张量。
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
df = pd.DataFrame({'A': np.array([100, 105.4, 108.3, 111.1, 113, 114.7]),
'B': np.array([11, 11.8, 12.3, 12.8, 13.1,13.6]),
'C': np.array([55, 56.3, 57, 58, 59.5, 60.4]),
'Target': np.array([4000, 4200.34, 4700, 5300, 5800, 6400])})
def data():
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:, :3].values,
df.iloc[:, 3].values,
test_size=0.2,
random_state=134)
return X_train, X_test, y_train, y_test
X_train, X_test, y_train, y_test = data()
features = {'A': X_train[:, 0],
'B': X_train[:, 1],
'C': X_train[:, 2]}
labels = y_train
batch_size = 1
def train_input_fn(features, labels, batch_size):
train_dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
train_dataset = train_dataset.shuffle(1000).repeat().batch(batch_size)
return train_dataset
def pack_features_vector(features, labels):
'''Pack the features into a single array'''
features = tf.stack(list(features.values()), axis=1)
return features, labels
train_dataset = train_input_fn(features, labels, batch_size).map(pack_features_vector)
class Model():
def __init__(self):
pass
def build_model(self, features):
inputs = tf.keras.Input(shape=(features.shape[1],))
x = tf.keras.layers.Dense(2, activation='relu')(inputs)
preds = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs=inputs, outputs=preds)
return model
def loss(self, loss_object, X, y):
ypred = self.build_model(X)
print(type(ypred))
print(ypred)
return loss_object(y_true=y, y_pred=ypred)
def grad(self, loss_object, X, y):
with tf.GradientTape() as tape:
loss_value = self.loss(loss_object, X, y)
return loss_value, tape.gradient(loss_value, self.build_model(X).trainable_variables)
def train(self, X, y, optimizer, loss_object):
loss_value, grads = self.grad(loss_object, X, y)
optimizer.apply_gradients(zip(grads, self.build_model(X).trainable_variables))
learning_rate = 0.001
optimizer=tf.optimizers.RMSprop(learning_rate)
loss_object=tf.keras.losses.mean_squared_error
epochs = 1
for epoch in range(epochs):
epoch_loss_avg = tf.keras.metrics.Mean()
epoch_acc = tf.keras.metrics.MeanSquaredError()
for X, y in train_dataset:
Model().train(X, y, optimizer, loss_object)
如果我不使用 class 和 运行：
inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(2, activation='relu')(inputs)
preds = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs=inputs, outputs=preds)
for x, y in train_dataset:
ypred = model(x)
print(type(ypred))
loss_object(y, ypred)
运行没问题！
model(x)的类型是<class 'tensorflow.python.framework.ops.EagerTensor'>
但是在class代码中，self.build_model(X)的类型是model。
在方法中将第一行从 ypred = self.build_model(X) 更改为 ypred = self.build_model()(X)
使用您的数据设置 "works" 的另一种方法：
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
df = pd.DataFrame({'A': np.array([100, 105.4, 108.3, 111.1, 113, 114.7]),
'B': np.array([11, 11.8, 12.3, 12.8, 13.1,13.6]),
'C': np.array([55, 56.3, 57, 58, 59.5, 60.4]),
'Target': np.array([4000, 4200.34, 4700, 5300, 5800, 6400])})
def data():
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:, :3].values,
df.iloc[:, 3].values,
test_size=0.2,
random_state=134)
return X_train, X_test, y_train, y_test
X_train, X_test, y_train, y_test = data()
features = {'A': X_train[:, 0],
'B': X_train[:, 1],
'C': X_train[:, 2]}
labels = y_train
batch_size = 1
def train_input_fn(features, labels, batch_size):
train_dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
train_dataset = train_dataset.shuffle(1000).repeat().batch(batch_size)
return train_dataset
def pack_features_vector(features, labels):
'''Pack the features into a single array'''
features = tf.stack(list(features.values()), axis=1)
return features, labels
train_dataset = train_input_fn(features, labels, batch_size).map(pack_features_vector)
class Model(tf.keras.Model):
def __init__(self):
super(Model,self).__init__()
self.l1= tf.keras.layers.Dense(2, activation='relu')
self.out = tf.keras.layers.Dense(1)
def __call__(self,x):
x=self.l1(x)
return self.out(x)
learning_rate = 1
optimizer=tf.optimizers.RMSprop(learning_rate)
loss_object=tf.keras.losses.mean_squared_error
model = Model()
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
for x,y in train_dataset:
with tf.GradientTape() as tape:
y_ = model(x)
loss = loss_object(y, y_)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
print("loss",train_loss(loss),"accuracy",train_accuracy(y,y_))