Closed
Description
I am trying to implement Grad-CAM using a custom model that includes ResNet50 as its backbone. When I attempt to build a model to extract the activations of the final convolutional layer and the final predictions, 1 encounter a KeyError during the model call.
Error Message
KeyError: 'Exception encountered when calling
Arguments received by Functional.call():
• inputs=tf. Tensor(shape=(1, 224, 224, 3),
• training=None
• mask=None'
My custom model is constructed as follows:
from
tensorflow.keras.applications import ResNet50
# Custom model
inputs = Input (shape=(224, 224, 3))
resnet_model = ResNet50(include_top=False, weights="imagenet", pooling="avg")
x = resnet_model (inputs)
outputs = Dense(1, activation="sigmoid") (x)
model = Model(inputs=inputs, outputs=outputs, name="custom_model")
And
import tensorflow as tf
from tensorflow.keras.models import Model
def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
# Create the grad_model using correct inputs and outputs
grad_model = Model(
inputs=model.input,
outputs=[
model.get_layer("resnet50").get_layer(last_conv_layer_name).output,
model.output
]
)
# Trace the gradient
with tf.GradientTape() as tape:
last_conv_layer_output, preds = grad_model(img_array)
.....
When the code arrives in grad_model(img_array) raises the error.
How can I fix it?
Thank you.