修正layer_name = 'block1_conv2'

原始程式碼:
layer_name = 'block1_conv2' layer = layer_dict[layer_name] activations = get_activations(vgg16, layer, input_img_data)

出現錯誤:
ValueError Traceback (most recent call last) Cell In[35], line 3 1 layer_name = 'block1_conv2' 2 layer = layer_dict[layer_name] ----> 3 activations = get_activations(vgg16, layer, input_img_data) Cell In[34], line 2, in get_activations(model, layer, input_img_data) 1 def get_activations(model, layer, input_img_data): ----> 2 activations_f = K.function([model.layers[0].input, K.learning_phase()], [layer.output,]) 3 activations = activations_f((input_img_data, False)) 4 return activations File C:\ProgramData\Anaconda3\lib\site-packages\keras\src\backend.py:4656, in function(inputs, outputs, updates, name, **kwargs) 4650 raise ValueError( 4651 "`updates` argument is not supported during " 4652 "eager execution. You passed: %s" % (updates,) 4653 ) 4654 from keras.src import models -> 4656 model = models.Model(inputs=inputs, outputs=outputs) 4658 wrap_outputs = isinstance(outputs, list) and len(outputs) == 1 4660 def func(model_inputs): File C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\trackable\base.py:204, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 202 self._self_setattr_tracking = False # pylint: disable=protected-access 203 try: --> 204 result = method(self, *args, **kwargs) 205 finally: 206 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File C:\ProgramData\Anaconda3\lib\site-packages\keras\src\engine\functional.py:159, in Functional.__init__(self, inputs, outputs, name, trainable, **kwargs) 153 # Check if the inputs contain any intermediate `KerasTensor` (not 154 # created by tf.keras.Input()). In this case we need to clone the `Node` 155 # and `KerasTensor` objects to mimic rebuilding a new model from new 156 # inputs. This feature is only enabled in TF2 not in v1 graph mode. 157 if tf.compat.v1.executing_eagerly_outside_functions(): 158 if not all( --> 159 [ 160 functional_utils.is_input_keras_tensor(t) 161 for t in tf.nest.flatten(inputs) 162 ] 163 ): 164 inputs, outputs = functional_utils.clone_graph_nodes( 165 inputs, outputs 166 ) 167 self._init_graph_network(inputs, outputs) File C:\ProgramData\Anaconda3\lib\site-packages\keras\src\engine\functional.py:160, in <listcomp>(.0) 153 # Check if the inputs contain any intermediate `KerasTensor` (not 154 # created by tf.keras.Input()). In this case we need to clone the `Node` 155 # and `KerasTensor` objects to mimic rebuilding a new model from new 156 # inputs. This feature is only enabled in TF2 not in v1 graph mode. 157 if tf.compat.v1.executing_eagerly_outside_functions(): 158 if not all( 159 [ --> 160 functional_utils.is_input_keras_tensor(t) 161 for t in tf.nest.flatten(inputs) 162 ] 163 ): 164 inputs, outputs = functional_utils.clone_graph_nodes( 165 inputs, outputs 166 ) 167 self._init_graph_network(inputs, outputs) File C:\ProgramData\Anaconda3\lib\site-packages\keras\src\engine\functional_utils.py:48, in is_input_keras_tensor(tensor) 32 """Check if tensor is directly generated from `tf.keras.Input`. 33 34 This check is useful when constructing the functional model, since we will (...) 45 ValueError: if the tensor is not a KerasTensor instance. 46 """ 47 if not node_module.is_keras_tensor(tensor): ---> 48 raise ValueError(_KERAS_TENSOR_TYPE_CHECK_ERROR_MSG.format(tensor)) 49 return tensor.node.is_input ValueError: Found unexpected instance while processing input tensors for keras functional model. Expecting KerasTensor which is from tf.keras.Input() or output from keras layer call(). Got: 0

改成這樣就好了
import tensorflow as tf def get_activations(model, layer, input_img_data): activations_f = tf.keras.backend.function(model.input, layer.output) activations = activations_f(input_img_data) return activations

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原始程式碼: def generate_pattern(layer_name, filter_index, size=150):     # 構建一個最大化激活的 損失函數  loss function     # 考慮的層的 第n個 filter     layer_output = model.get_layer(layer_name). output     loss = K.mean(layer_output[:, :, :, filter_index])     # 計算這種損失的輸入圖像的梯度     grads = K.gradients(loss, model.input)[0]     # Normalization gradient 梯度     grads /= (K.sqrt(K.mean(K.square(grads) )) + 1e-5)     # 函數返回 給定 輸入圖片的 損失 和 梯度     iterate = K.function([model.input], [loss, grads])         # 帶有一些噪音的灰色圖像     input_img_data = np.random.random((1, size, size, 3)) * 20 + 128.     # Run 梯度上升 40步     step = 1.     for i in range(40):         loss_value, grads_value = iterate([input_img_data])         input_img_data += grads_value * step             img = input_img_da...
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修正input_img_data = np.random.random((1, 150, 150, 3)) * 20 + 128.

原始碼如下: # 灰色圖像 帶有一些噪音 input_img_data = np.random.random((1, 150, 150, 3)) * 20 + 128. # 40 steps forgradient ascent step = 1. # 每個梯度更新的幅度 for i in range(40): # 計算損失值和梯度值 loss_value, grads_value = iterate([input_img_data]) # 調整輸入圖像的方向，使損失最大化 input_img_data += grads_value * step 出現錯誤: NameError Traceback (most recent call last) Cell In[30], line 9 6 step = 1. # 每個梯度更新的幅度 7 for i in range(40): 8 # 計算損失值和梯度值 ----> 9 loss_value, grads_value = iterate([input_img_data]) 10 # 調整輸入圖像的方向，使損失最大化 11 input_img_data += grads_value * step   NameError: name 'iterate' is not defined   修正成這樣就可以了 import numpy as np import tensorflow as tf from tensorflow.keras import optimizers # The rest of your code remains the same # Compile your model before using it in the gradient computation model.compile(loss='binary_crossentropy',               optimizer=optimizers.RMSpr...
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緣起

         一如往常，還是要說明一下為什麼開始寫這個部落格，主要的原因是過去這半年接觸AI之後一直遇到大大小小的電腦問題。做為一個門外漢，要把電腦設定到網路那些影片教學可以運作的狀態的的是會有超多問題的，所以就用個部落格記錄這段歷史，好勸退想不開想踏入AI的如我一般的外行人（大誤）
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