Question

我正在尝试使用tflearn

训练，保存和加载张量流模型

Sub testy2ElectricBoogaloo()
    dim i as long, ans as boolean
    Dim mystr As String
    ans = False
    ReDim arr(1 To Worksheets.Count, 1 To 2)
    For i = 1 To UBound(arr)
     arr(i, 1) = Worksheets(i).Name
    'My code makes every password simply the sheet name followed by a smiley face.
    'Adjust to fit your actual passwords.
     arr(i, 2) = Worksheets(i).Name & " :)" 
    Next i
    Do While ans = False
        mystr = InputBox("Please enter password to continue.", "Enter Password")
        If mystr = vbNullString Then Exit Sub
        For i = 1 To ThisWorkbook.Worksheets.Count
        If mystr = arr(i, 2) Then ans = True: Worksheets(arr(i, 1)).Visible = True: Exit For
        Next i
    Loop
End Sub

这部分有效。接下来，我做

        # Building convolutional network

        network = input_data(shape=[None, imageSize, imageSize, 1], name='input')
        network = conv_2d(network, imageSize, self.windowSize, activation='relu', regularizer="L2")
        network = max_pool_2d(network, 2)
        network = local_response_normalization(network)
        network = conv_2d(network, imageSize * 2, self.windowSize, activation='relu', regularizer="L2")
        network = max_pool_2d(network, 2)
        network = local_response_normalization(network)
        network = fully_connected(network, (dim4 * dim4) * (imageSize * 2), activation='tanh')
        network = dropout(network, keep)
        network = fully_connected(network, (dim4 * dim4) * (imageSize * 2), activation='tanh')
        network = dropout(network, keep)
        network = fully_connected(network, n_classes, activation='softmax')
        network = regression(network, optimizer='adam', learning_rate=self.learningRate,
                                loss='categorical_crossentropy', name='target')

        model = tflearn.DNN(network, tensorboard_verbose=0, tensorboard_dir='some/dir')
        model.fit(

            {'input': np.array(myData.train_x).reshape(-1, self.imageSize, self.imageSize, 1)}, {'target': myData.train_y}, n_epoch=self.epochs,

            validation_set=(
                {'input': np.array(myData.test_x).reshape(-1, self.imageSize, self.imageSize, 1)},
            {'target': myData.test_y}),
        snapshot_step=100, show_metric=True, run_id='convnet')
        model.save("some/path/model")

这在model_path = "some/path/model.meta" if os.path.exists(model_path): model.load(model_path) else : return "need to train the model" prediction = self.model.predict([<some_input>]) print(str(prediction)) return prediction失败了。我得到以下错误跟踪

model.load(model_path)

是什么意思

DataLossError (see above for traceback): Unable to open table file some/path/model.meta: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator?
     [[Node: save_5/RestoreV2_4 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save_5/Const_0_0, save_5/RestoreV2_4/tensor_names, save_5/RestoreV2_4/shape_and_slices)]]
Caused by op 'save_5/RestoreV2_4', defined at:

我可以看到模型确实正确保存并且不是空文件。为什么我不能加载它？

版本信息

Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator?

Answer 1

答案：

如评论中所述，保存变量的路径必须包含“.ckpt”文件名。

同样，应通过相同的“.ckpt”文件进行恢复

TensorFlow：无法加载训练模型

1 个答案: