当尝试在Android studio中实现tensorflow lite模型时，我得到一个不兼容的数据类型错误

Question

java.lang.IllegalArgumentException: Cannot convert between a TensorFlowLite tensor with type INT64 and a Java object of type [[F (which is compatible with the TensorFlowLite type FLOAT32).

import io
df = pd.read_csv(io.BytesIO(data_to_load['species_by_location_v4.csv']))

# CREATE X ARRAY
# This is the array containing the explanatory variables (in this case pentad and month)

loc_array = df.iloc[:, 1:3]
print(loc_array)

# create y array (classes to be predicted)

label_array = df.iloc[:, 4]

# get number of distinct classes and convert y array to consecutive integers from 0 to 170 (y_true)

raw_y_true = label_array
mapping_to_numbers = {}
y_true = np.zeros((len(raw_y_true)))
for i, raw_label in enumerate(raw_y_true):
    if raw_label not in mapping_to_numbers:
        mapping_to_numbers[raw_label] = len(mapping_to_numbers)
    y_true[i] = mapping_to_numbers[raw_label]
print(y_true)
# [0. 1. 2. 3. 1. 2.]
print(mapping_to_numbers)

# get number of distinct classes

num_classes = len(mapping_to_numbers)
print(num_classes)


# create simple model

model = tf.keras.Sequential([
  tf.keras.layers.Dense(num_classes, activation='softmax')
])

# compile model

model.compile(
    optimizer = 'adam',
    loss='sparse_categorical_crossentropy', 
    metrics=['accuracy']
)

# train model

history = model.fit(loc_array, y_true, epochs=500, verbose=False)
print('finished')

# create labels file

labels = '\n'.join(mapping_to_numbers.keys())

with open('labels_locmth.txt', 'w') as f:
  f.write(labels)

!cat labels.txt

# convert to tflite

saved_model_dir = 'save/fine_tuning'
tf.saved_model.save(model, saved_model_dir)

converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()

with open('model_locmth.tflite', 'wb') as f:
  f.write(tflite_model)

temp = {'pentad_unique_key': [63669], 'mth': [2] }
test_x = pd.DataFrame(temp, columns = ['pentad_unique_key', 'mth'])
result = model.predict(test_x, batch_size=None, verbose=0, steps=None)

float[][] inputVal = new float[1][2];
inputVal[0][0] = 63669;
inputVal[0][1] = 2;

float[][] outputs = new float[1][171];

tflite.run(inputVal, outputs);

try{
   tflite = new Interpreter(loadModelFile());
   labelList = loadLabelList();
} catch (Exception ex) {
   ex.printStackTrace();
}

private MappedByteBuffer loadModelFile() throws IOException {

    // Open the model using an input stream and memory map it to load
    AssetFileDescriptor fileDescriptor = this.getAssets().openFd("model_locmth.tflite");
    FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());
    FileChannel fileChannel = inputStream.getChannel();
    long startOffset = fileDescriptor.getStartOffset();
    long declaredLength = fileDescriptor.getDeclaredLength();
    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);

}

Answer 1

如前所述，我认为问题在于y_true的输入dtype。当您将y_true创建为

y_true = np.zeros((len(raw_y_true)))

它会将dtype显示为np.int64，因为它是默认的dtype。
tflite模型可以使用错误中提到的float32（不是int64或float64)，所以您需要将y_true定义为

y_true = np.zeros((len(raw_y_true)),dtype=np.float32)

因此，我只在代码的以下部分更改了上面的行

label_array = df.iloc[:, 4]

raw_y_true = label_array
mapping_to_numbers = {}
y_true = np.zeros((len(raw_y_true)),dtype=np.float32)
for i, raw_label in enumerate(raw_y_true):
    if raw_label not in mapping_to_numbers:
        mapping_to_numbers[raw_label] = len(mapping_to_numbers)
    y_true[i] = mapping_to_numbers[raw_label]
print(y_true)
# [0. 1. 2. 3. 1. 2.]
print(mapping_to_numbers)

print(np.argmax(result)) # 122