I have a problem to visualize the images when run into my local machine it says this error:
The layer sequential_5 has never been called and thus has no defined input.
the initial code is below:
import numpy as np
import random
from tensorflow.keras.utils import img_to_array, load_img
Define a new Model that will take an image as input, and will output
intermediate representations for all layers in the previous model
successive_outputs = [layer.output for layer in model.layers]
visualization_model = tf.keras.models.Model(inputs = model.input, outputs = successive_outputs)
Prepare a random input image from the training set.
cat_img_files = [os.path.join(train_cats_dir, f) for f in train_cat_fnames]
dog_img_files = [os.path.join(train_dogs_dir, f) for f in train_dog_fnames]
img_path = random.choice(cat_img_files + dog_img_files)
img = load_img(img_path, target_size=(150, 150)) # this is a PIL image
x = img_to_array(img) # Numpy array with shape (150, 150, 3)
x = x.reshape((1,) + x.shape) # Numpy array with shape (1, 150, 150, 3)
Scale by 1/255
x /= 255.0
Run the image through the network, thus obtaining all
intermediate representations for this image.
successive_feature_maps = visualization_model.predict(x)
These are the names of the layers, so you can have them as part of our plot
layer_names = [layer.name for layer in model.layers]
Display the representations
for layer_name, feature_map in zip(layer_names, successive_feature_maps):
if len(feature_map.shape) == 4:
#-------------------------------------------
# Just do this for the conv / maxpool layers, not the fully-connected layers
#-------------------------------------------
n_features = feature_map.shape[-1] # number of features in the feature map
size = feature_map.shape[ 1] # feature map shape (1, size, size, n_features)
# Tile the images in this matrix
display_grid = np.zeros((size, size * n_features))
#-------------------------------------------------
# Postprocess the feature to be visually palatable
#-------------------------------------------------
for i in range(n_features):
x = feature_map[0, :, :, i]
x -= x.mean()
x /= x.std ()
x *= 64
x += 128
x = np.clip(x, 0, 255).astype('uint8')
display_grid[:, i * size : (i + 1) * size] = x # Tile each filter into a horizontal grid
#-----------------
# Display the grid
#-----------------
scale = 20. / n_features
plt.figure( figsize=(scale * n_features, scale) )
plt.title ( layer_name )
plt.grid ( False )
plt.imshow( display_grid, aspect='auto', cmap='viridis' )
I have no problem when run into google colab so I am curious I search into and re-run the model few times but still I cant visualize
please guide
