keras: ValueError:未能找到可以处理输入的数据适配器

Question

我有一个深度学习模型，我试图用简单的输入进行测试。在这一行:

history = model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))

我得到这个错误:

Traceback (most recent call last):
File "/usr/lib64/python3.6/contextlib.py", line 99, in __exit__
self.gen.throw(type, value, traceback)
File "/home/miran045/reine097/projects/ResNet34/venv/lib64/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 2833, in variable_creator_scope
yield
File "/home/miran045/reine097/projects/ResNet34/venv/lib/python3.6/site-packages/keras/engine/training.py", line 1148, in fit
steps_per_execution=self._steps_per_execution)
File "/home/miran045/reine097/projects/ResNet34/venv/lib/python3.6/site-packages/keras/engine/data_adapter.py", line 1383, in get_data_handler
return DataHandler(*args, **kwargs)
File "/home/miran045/reine097/projects/ResNet34/venv/lib/python3.6/site-packages/keras/engine/data_adapter.py", line 1137, in __init__
adapter_cls = select_data_adapter(x, y)
File "/home/miran045/reine097/projects/ResNet34/venv/lib/python3.6/site-packages/keras/engine/data_adapter.py", line 979, in select_data_adapter
_type_name(x), _type_name(y)))
ValueError: Failed to find data adapter that can handle input: (<class 'list'> containing values of types {"<class 'numpy.ndarray'>"}), (<class 'list'> containing values of types {"<class 'int'>"})
python-BaseException

代码如下:

from functools import partial
import numpy as np
import tensorflow as tf
from tensorflow import keras
DefaultConv3D = partial(keras.layers.Conv3D, kernel_size=3, strides=1,
padding="SAME", use_bias=False)

class ResidualUnit(keras.layers.Layer):
def __init__(self, filters, strides=1, activation="relu", **kwargs):
super().__init__(**kwargs)
self.activation = keras.activations.get(activation)
self.main_layers = [
DefaultConv3D(filters, strides=strides),
keras.layers.BatchNormalization(),
self.activation,
DefaultConv3D(filters),
keras.layers.BatchNormalization()]
self.skip_layers = []
if strides > 1:
self.skip_layers = [
DefaultConv3D(filters, kernel_size=1, strides=strides),
keras.layers.BatchNormalization()]
def call(self, inputs):
Z = inputs
for layer in self.main_layers:
Z = layer(Z)
skip_Z = inputs
for layer in self.skip_layers:
skip_Z = layer(skip_Z)
return self.activation(Z + skip_Z)

def get_model():
model = keras.models.Sequential()
model.add(DefaultConv3D(64, kernel_size=7, strides=2,
input_shape=[1, 182, 218, 182]))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation("relu"))
model.add(keras.layers.MaxPool3D(pool_size=3, strides=2, padding="SAME"))
prev_filters = 64
for filters in [64] * 3 + [128] * 4 + [256] * 6 + [512] * 3:
strides = 1 if filters == prev_filters else 2
model.add(ResidualUnit(filters, strides=strides))
prev_filters = filters
model.add(keras.layers.GlobalAvgPool3D())
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(1))
return model

def run():
model = get_model()
model.compile(loss="mean_squared_error", optimizer="adam", metrics=[tf.keras.metrics.MeanSquaredError()])
x1 = np.random.rand(182, 218, 182)
x2 = np.random.rand(182, 218, 182)
x3 = np.random.rand(182, 218, 182)
x4 = np.random.rand(182, 218, 182)
x5 = np.random.rand(182, 218, 182)
x6 = np.random.rand(182, 218, 182)
X_train = [x1, x2, x3]
y_train = [2]
X_valid = [x4]
y_valid = [2]
X_test = [x5]
y_test = [3]
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))
print(history)
score = model.evaluate(X_test, y_test)
print(score)
X_new = x6
y_pred = model.predict(X_new)
print(y_pred)

if __name__ == '__main__':
run()

我在这里做错了什么?我如何转换我的训练和验证数据，使其工作。

Answer 1

您的x数据以及您的y数据需要是数组并且具有相同数量的样本。下面是一个例子:

model = get_model()
model.compile(loss="mean_squared_error", optimizer="adam", metrics=[tf.keras.metrics.MeanSquaredError()])
x1 = np.random.rand(1, 182, 218, 182)
x2 = np.random.rand(1, 182, 218, 182)
x3 = np.random.rand(1, 182, 218, 182)
X_train = np.stack([x1, x2, x3])
y_train = np.array([2, 2, 2])
history = model.fit(X_train, y_train, epochs=1)

X_train有3个样本，y_train也有。