将形状转换为 TensorShape 时出错：维度值必须是整数或无或具有 __index__ 方法，得到值'

Question

我正在为使用 RGB 图像的回归编写一个简单的卷积网络，但我遇到了输入形状的问题。 形状是 (8,96,96,3) 但错误告诉我它不是一个正常的整数，因为它是一个元组。 我有一个相对较小的数据集。 这是代码和错误。

编码：

import kerastuner
from keras.models import load_model
from kerastuner.tuners import RandomSearch
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Activation
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from imutils import paths
import numpy as np
import argparse
import cv2
import os
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from tensorflow.keras.models import Sequential
from keras.utils.vis_utils import plot_model
from keras.layers import Input
from keras.models import Model
from tensorflow import keras
from google.colab.patches import cv2_imshow
from google.colab import drive
drive.mount('/content/drive')

LABELS = set(["56", "76", "72", "110"])

print("[INFO] loading images...")
imagePaths = list(paths.list_images('/content/drive/My Drive/train'))
data = []
labels = []

for imagePath in imagePaths:
    label = imagePath.split(os.path.sep)[-2]

    if label not in LABELS:
        continue
    # load the image and resize it to be a fixed 96x96 pixels,
    # ignoring aspect ratio
    image = cv2.imread(imagePath)
    image = cv2.resize(image, (96, 96))
    # update the data and labels lists, respectively
    data.append(image)
    labels.append(label)
labels=list(set(labels))


data = np.array(data, dtype="float") / 250
labels = np.array(labels, dtype="int") 

(X, Y) = (data, labels)

LABELS = set(["59"])

imagePaths = list(paths.list_images('/content/drive/My Drive/test'))
data = []
labels = []

for imagePath in imagePaths:
    label = imagePath.split(os.path.sep)[-2]

    if label not in LABELS:
        continue
    image = cv2.imread(imagePath)
    image = cv2.resize(image, (96, 96))
    data.append(image)
    labels.append(label)

labels=list(set(labels))

data = np.array(data, dtype="float") / 250
labels = np.array(labels, dtype="int") 

(Z,T) =(data,labels)

import numpy as np
from sklearn.preprocessing import StandardScaler


aug = ImageDataGenerator(rotation_range=20, zoom_range=0.15,
    width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15,
    horizontal_flip=True, fill_mode="nearest")

import tensorflow as tf
X.reshape(8,96,96,3)
input_shape =Input(batch_shape=[8,96,96,3])
Z.reshape(2,96,96,3)
X=np.array(X, dtype="int")
Z=np.array(Z, dtype="int")
def create_model(hp):
 model = Sequential()

 model.add(keras.layers.Conv2D(hp.Int('a',32,256,step=1), (3,3), input_shape=input_shape,activation='relu'))
 
 model.add(MaxPooling2D(pool_size=(2, 2)))

 
 model.add(keras.layers.Conv2D(hp.Int('c',32,256,step=1), (3,3), activation='relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))

 model.add(keras.layers.Conv2D(hp.Int('w',32,256,step=1), (3,3), activation='relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Flatten())
 opt = Adam(lr=1e-3, decay=1e-3 / 200)
 model.compile(loss="mean_absolute_percentage_error", optimizer=opt)

 model.fit(x=aug.flow(X, Y, batch_size=5),validation_data=(Z, T), steps_per_epoch=5, epochs=hp.Int('n',30,31,step=1))

 return model

np.set_printoptions(precision=2,suppress=True)
my_dir ='/content/drive/My Drive/'
tuner= RandomSearch(create_model,objective= 'val_loss',  max_trials =1, executions_per_trial = 1, directory = 'my_dir' )

tuner.search(x=X, y=Y,epochs=30,batch_size=5,validation_data=(Z, T))

create_model(tuner.get_best_hyperparameters()[0]).save('model1.h5')

model1=keras.models.load_model('model1.h5')
for i in range(len(Z)):
    print("Prediction: " + str(model1.predict(Z[i] )) + " | True: " + str(T[i]))

scores=model1.evaluate(Z,T,batch_size=5)
print(scores)
tf.keras.utils.plot_model(model1, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
print(tuner.get_best_hyperparameters()[0].values)

错误：

  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py", line 213, in make_shape
    raise TypeError("Error converting %s to a TensorShape: %s." % (arg_name, e))
TypeError: Error converting shape to a TensorShape: Dimension value must be integer or None or have an __index__ method, got value '<tf.Tensor 'strided_slice_40:0' shape=(96, 96, 3) dtype=float32>' with type '<class 'tensorflow.python.framework.ops.Tensor'>'.
[Warning] Invalid model 5/5
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in make_shape(v, arg_name)
    210   try:
--> 211     shape = tensor_shape.as_shape(v)
    212   except TypeError as e:

22 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py in as_shape(shape)
   1234   else:
-> 1235     return TensorShape(shape)
   1236 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py in __init__(self, dims)
    755     if isinstance(dims, (tuple, list)):  # Most common case.
--> 756       self._dims = [Dimension(d) for d in dims]
    757     elif dims is None:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py in <listcomp>(.0)
    755     if isinstance(dims, (tuple, list)):  # Most common case.
--> 756       self._dims = [Dimension(d) for d in dims]
    757     elif dims is None:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py in __init__(self, value)
    203                       "an __index__ method, got value '{0!r}' with type '{1!r}'"
--> 204                       .format(value, type(value))), None)
    205       if self._value < 0:

/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value)

TypeError: Dimension value must be integer or None or have an __index__ method, got value '<tf.Tensor 'strided_slice_40:0' shape=(96, 96, 3) dtype=float32>' with type '<class 'tensorflow.python.framework.ops.Tensor'>'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/kerastuner/engine/hypermodel.py in build(self, hp)
    104                 with maybe_distribute(self.distribution_strategy):
--> 105                     model = self.hypermodel.build(hp)
    106             except:

<ipython-input-16-7d8b77f7488e> in create_model(hp)
    114 
--> 115  model.add(keras.layers.Conv2D(hp.Int('a',32,256,step=1), (3,3), input_shape=input_shape,activation='relu'))
    116 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py in add(self, layer)
    201           x = input_layer.Input(
--> 202               batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
    203           # This will build the current layer

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_layer.py in Input(shape, batch_size, name, dtype, sparse, tensor, ragged, **kwargs)
    310         {'batch_size': batch_size, 'input_shape': shape})
--> 311   input_layer = InputLayer(**input_layer_config)
    312 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_layer.py in __init__(self, input_shape, batch_size, dtype, input_tensor, sparse, name, ragged, **kwargs)
    159             sparse=sparse,
--> 160             ragged=ragged)
    161 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py in placeholder(shape, ndim, dtype, sparse, name, ragged)
   1222       else:
-> 1223         x = array_ops.placeholder(dtype, shape=shape, name=name)
   1224 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py in placeholder(dtype, shape, name)
   3099 
-> 3100   return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
   3101 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py in placeholder(dtype, shape, name)
   6806     shape = None
-> 6807   shape = _execute.make_shape(shape, "shape")
   6808   _, _, _op, _outputs = _op_def_library._apply_op_helper(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in make_shape(v, arg_name)
    212   except TypeError as e:
--> 213     raise TypeError("Error converting %s to a TensorShape: %s." % (arg_name, e))
    214   except ValueError as e:

TypeError: Error converting shape to a TensorShape: Dimension value must be integer or None or have an __index__ method, got value '<tf.Tensor 'strided_slice_40:0' shape=(96, 96, 3) dtype=float32>' with type '<class 'tensorflow.python.framework.ops.Tensor'>'.

During handling of the above exception, another exception occurred:

RuntimeError                              Traceback (most recent call last)
<ipython-input-16-7d8b77f7488e> in <module>()
    133 np.set_printoptions(precision=2,suppress=True)
    134 my_dir ='/content/drive/My Drive/'
--> 135 tuner= RandomSearch(create_model,objective= 'val_loss',  max_trials =1, executions_per_trial = 1, directory = 'my_dir' )
    136 
    137 tuner.search(x=X, y=Y,epochs=30,batch_size=5,validation_data=(Z, T))

/usr/local/lib/python3.6/dist-packages/kerastuner/tuners/randomsearch.py in __init__(self, hypermodel, objective, max_trials, seed, hyperparameters, tune_new_entries, allow_new_entries, **kwargs)
    173             oracle,
    174             hypermodel,
--> 175             **kwargs)

/usr/local/lib/python3.6/dist-packages/kerastuner/engine/multi_execution_tuner.py in __init__(self, oracle, hypermodel, executions_per_trial, **kwargs)
     56                  **kwargs):
     57         super(MultiExecutionTuner, self).__init__(
---> 58             oracle, hypermodel, **kwargs)
     59         if isinstance(oracle.objective, list):
     60             raise ValueError(

/usr/local/lib/python3.6/dist-packages/kerastuner/engine/tuner.py in __init__(self, oracle, hypermodel, max_model_size, optimizer, loss, metrics, distribution_strategy, directory, project_name, logger, tuner_id, overwrite)
    101                                     project_name=project_name,
    102                                     logger=logger,
--> 103                                     overwrite=overwrite)
    104 
    105         self.distribution_strategy = distribution_strategy

/usr/local/lib/python3.6/dist-packages/kerastuner/engine/base_tuner.py in __init__(self, oracle, hypermodel, directory, project_name, logger, overwrite)
     89         self._display = tuner_utils.Display()
     90 
---> 91         self._populate_initial_space()
     92 
     93         if not overwrite and tf.io.gfile.exists(self._get_tuner_fname()):

/usr/local/lib/python3.6/dist-packages/kerastuner/engine/base_tuner.py in _populate_initial_space(self)
    104         """
    105         hp = self.oracle.get_space()
--> 106         self.hypermodel.build(hp)
    107         self.oracle.update_space(hp)
    108 

/usr/local/lib/python3.6/dist-packages/kerastuner/engine/hypermodel.py in _build_wrapper(self, hp, *args, **kwargs)
     63             # to the search space.
     64             hp = hp.copy()
---> 65         return self._build(hp, *args, **kwargs)
     66 
     67 

/usr/local/lib/python3.6/dist-packages/kerastuner/engine/hypermodel.py in build(self, hp)
    113                 if i == self._max_fail_streak:
    114                     raise RuntimeError(
--> 115                         'Too many failed attempts to build model.')
    116                 continue
    117 

RuntimeError: Too many failed attempts to build model.

Answer 1

您将input shape分配给tf.layer.Input()对象，而不是元组。

input_shape = Input(batch_shape=[8,96,96,3])

您可以通过执行以下操作来解决此问题，这将获取 Input 层的第一个输入的形状：

input_shape = Input(batch_shape=[8, 96, 96, 3]).input_shape()[0]

当然，既然你知道你的形状，我想你可以指定input_shape=(96, 96, 3) 。 您省略了第一个维度，因为批量大小是推断出来的。 有关示例，请参阅此 TF 教程。