世界您好！我們正在編寫自己的 AI，并且努力創建正確的模型層。我們必須在我們的神經網絡中輸入的是一個list包含 nlists和 mtuplese.x. list = numpy.array([ [[1,2,4],[5,6,8]] , [[5,6,0],[7,2,4]] ])我們期望得到的結果是 0 或 1（相信我，這是有道理的）這就是我們現在所擁有的：tpl = 3 # because we have tuplesnl = 2 # number of lists we havemodel = tf.keras.Sequential([# this should be entry layer that understands our list tf.keras.layers.Dense(nl * tpl , input_shape=(nl, tpl), activation='relu'),#hidden layers.. tf.keras.layers.Dense(64, input_shape=(nl, tpl), activation='sigmoid'),#our output layer with 2 nodes that one should contain 0, other 1, because we have 2 labels ( 0 and 1 ) tf.keras.layers.Dense(2, input_shape=(0, 1), activation='softmax') ])但是我們得到以下錯誤：/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 58 ctx.ensure_initialized() 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,---> 60 inputs, attrs, num_outputs) 61 except core._NotOkStatusException as e: 62 if name is not None:InvalidArgumentError: Incompatible shapes: [56,2,2] vs. [56,1] [[node huber_loss/Sub (defined at <ipython-input-25-08eb2e0b395e>:53) ]] [Op:__inference_train_function_45699]Function call stack:train_function如果我們總結我們的模型，它會給出以下結構：Layer (type) Output Shape Param # =================================================================dense_1 (Dense) (None, 2, 6) 24 _________________________________________________________________dense_2 (Dense) (None, 2, 64) 448 _________________________________________________________________dense_3 (Dense) (None, 2, 2) 130 =================================================================最后，我們了解到，我們提供的數據與最后一層不兼容，那么我們如何將最后一層轉換為 => shape (None, 2)或者解決此錯誤的正確方法是什么？
1 回答
森林海
TA貢獻2011條經驗 獲得超2個贊
您可以在輸出層之前使用Flatten()或。GlobalAveragePooling1D完整示例：
import numpy
import tensorflow as tf
list = numpy.array([[[1., 2., 4.], [5., 6., 8.]], [[5., 6., 0.], [7., 2., 4.]]])
tpl = 3
nl = 2
model = tf.keras.Sequential([
tf.keras.layers.Dense(nl * tpl, input_shape=(nl, tpl), activation='relu'),
tf.keras.layers.Dense(64, input_shape=(nl, tpl), activation='sigmoid'),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.Dense(2, input_shape=(0, 1), activation='softmax')
])
model.build(input_shape=(nl, tpl))
model(list)
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[0.41599566, 0.58400434],
[0.41397247, 0.58602756]], dtype=float32)>
你不會只得到 0 和 1，你會得到每個班級的概率。你也應該隱藏內置關鍵字list。
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_12 (Dense) (None, 2, 6) 24
_________________________________________________________________
dense_13 (Dense) (None, 2, 64) 448
_________________________________________________________________
global_average_pooling1d (Gl (None, 64) 0
_________________________________________________________________
dense_14 (Dense) (None, 2) 130
=================================================================
Total params: 602
Trainable params: 602
Non-trainable params: 0
_________________________________________________________________
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