predict_tf.py

TF_INPUT_TENSOR = 'input:0'
TF_OUTPUT_TENSOR = 'MobilenetV2/Predictions/Reshape_1:0'

with tf.Session(graph = g) as sess:
    tf_laptop_out = sess.run(TF_OUTPUT_TENSOR, feed_dict={TF_INPUT_TENSOR: img_laptop_tf})
    tf_golden_out = sess.run(TF_OUTPUT_TENSOR, feed_dict={TF_INPUT_TENSOR: img_golden_tf})
tf_laptop_out = tf_laptop_out.flatten()    
tf_golden_out = tf_golden_out.flatten()    
laptop_idx = np.argmax(tf_laptop_out)
golden_idx = np.argmax(tf_golden_out)

print("Prediction for Golden Retriever:",
      labels[golden_idx], 
      str(tf_golden_out[golden_idx]))
print("Prediction for laptop:",
      labels[laptop_idx], 
      str(tf_laptop_out[laptop_idx]))