如何更改解释器输出形状？

Question

我正在尝试在 android 上构建一个 tflite 文件。

所以我使用 jupyter notebook 创建了以下模型。

将创建的模型转换为tflite文件后，我们检查它是否正确转换。

我希望输出解释器形状结果是1this，但结果还是1[10]我该怎么办？

我制作这样的模型层。

model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(32, (3,3), padding="same", input_shape=X_train.shape[1:], activation="relu"),
  tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
  tf.keras.layers.Conv2D(32, (3,3), padding="same", activation="relu"),
  tf.keras.layers.MaxPooling2D(pool_size=(2,2)),

  tf.keras.layers.Conv2D(64, (3,3), padding="same", activation="relu"),
  tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
  tf.keras.layers.Dropout(0.25),

  tf.keras.layers.Conv2D(64, (3,3), padding="same", activation="relu"),
  tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
  tf.keras.layers.Dropout(0.25),

  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(256, activation="relu"),
  tf.keras.layers.Dropout(0.5),
  tf.keras.layers.Dense(1, activation="sigmoid")
])

将训练好的模型转换为 tflite 文件的一部分

model = tf.keras.models.load_model("./model/model.h5")
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)


interpreter = tf.lite.Interpreter(model_path="converted_model.tflite")
interpreter.allocate_tensors()

# Get input and output tensors.
input_details = interpreter.get_input_details()
print("--------------")
print("shape:", input_details[0]['shape'])
print("type:", input_details[0]['dtype'])
output_details = interpreter.get_output_details()
print("--------------")
print("shape:", output_details[0]['shape'])
print("type:", output_details[0]['dtype'])

interpreter.resize_tensor_input(input_details[0]['index'], (39, 64, 64))
interpreter.resize_tensor_input(output_details[0]['index'], (39, 5))
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
print("--------------")
print("shape:", input_details[0]['shape'])
print("type:", input_details[0]['dtype'])
output_details = interpreter.get_output_details()
print("--------------")
print("shape:", output_details[0]['shape'])
print("type:", output_details[0]['dtype'])

enter image description here

Answer 1

使用 Keras 创建模型时，您可以使用：

from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model

# some stuff your code does

input = Input((THE_HEIGHT_YOU_WANT, THE_WIDTH_YOU_WANT, THE_CHANNELS_YOU_WANT))

# all the Tensorflow ops you want on "input"

model = Model(input, THE_OUTPUT_YOU_WANT)

# any other stuff your code might do before saving

model.save(THE_PATH_YOU_WANT)

loaded_model = tf.keras.models.load_model(THE_PATH_YOU_WANT)

# rest of your code for converting and saving the model

现在在 Android 上运行时，您可以使用：

tensorflowLiteInterpreterInstance.getInputTensor(inputTensorIndex).shape()

获取模型的形状。

形状应与(THE_HEIGHT_YOU_WANT, THE_WIDTH_YOU_WANT, THE_CHANNELS_YOU_WANT) 形状匹配。