How to generate the layer heat map in Keras 2.1

generate_layer_heat_map.py

def get_heatmap(model, layer_name, matrix, y_labels):

    # obtain probability of the label with the highest certainty
    network_output = model.get_output_at(0)[:, np.argmax(y_labels)]
    # obtain the output vector and its dimension of the convolutional layer we want to visualize
    conv_layer, layer_output_dim = get_conv_layer(model, layer_name)
    
    # Setting up the calculation of the gradients between the output and the conv layer. Will be executed in the iteration step
    grads = K.gradients(network_output, conv_layer.output)[0]
    # average the gradients across our samples (one sample) and all filters
    pooled_grads = K.mean(grads, axis=(0, 2))
    
    # set up the computation graph
    iterate = K.function([model.get_input_at(0)], [pooled_grads, conv_layer.output[0]])
    # execute the computation graph with our converted document (matrix) as an input
    pooled_grad_value, conv_layer_output_value = iterate([matrix]) 
    
    # loop over every layer output vector element and multiply it by the gradient of the element
    for i in range(layer_output_dim):
        conv_layer_output_value[i] *= pooled_grad_value[i]
    
    # calculating the average output value for each output dimension across all filters
    heatmap = np.mean(conv_layer_output_value, axis=-1)
    return norm_heatmap(heatmap)

When i try to use your code ... i get the following error
ValueError: Tensor Tensor("Mean:0", shape=(4498,), dtype=float32) is not an element of this graph.

Hi @sarim-zafar which TF version were you using?