I am have a keras model that is supposed to take a (150, 150, 1)
grayscale image as it’s input and output an array of length 8.
Here is my model code:
from tensorflow.python import keras model = keras.Sequential([ keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation="relu", padding='same', input_shape=(150,150,1)), keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'), keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'), keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'), keras.layers.Flatten(), keras.layers.Dense(8, activation="softmax") ])
When I try to use the .predict()
method, I get this error:
Traceback (most recent call last): File "KerasCNN.py", line 152, in <module> ga.run() File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/pygad/pygad.py", line 1192, in run self.last_generation_fitness = self.cal_pop_fitness() File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/pygad/pygad.py", line 1159, in cal_pop_fitness fitness = self.fitness_func(sol, sol_idx) File "KerasCNN.py", line 112, in fitness prediction = model.predict(g_img) File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/models.py", line 966, in predict return self.model.predict(x, batch_size=batch_size, verbose=verbose) File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/engine/training.py", line 1813, in predict f, ins, batch_size=batch_size, verbose=verbose, steps=steps) File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/engine/training.py", line 1300, in _predict_loop index_array = np.arange(num_samples) TypeError: unsupported operand type(s) for /: 'Dimension' and 'int'
I had an ANN (non-CNN) model running earlier that was working fine. When I did some research I could find anything about this error either.
Here is the code I am using to make the prediction:
img = get_image() # (150, 150, 3) g_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # (150, 150, 1) g_img = tf.expand_dim(g_img, axis=0) g_img = tf.expand_dim(g_img, axis=-1) # (1, 150, 150, 1) prediction = model.predict(g_img)
Here are my version numbers:
tensorflow: 1.5.0
python: 3.69
numpy: 1.19.5
Ubuntu: 18.04
Let me know if theres any other info I can provide! Thanks!
Answer
Replacing tf.expand_dim()
with np.expand_dim()
fixed it!
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Answer
This seems to run perfectly fine on TF 1.15:
import cv2 import numpy as np import tensorflow as tf from tensorflow.python import keras print(tf.__version__) model = keras.Sequential([ keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation="relu", padding='same', input_shape=(150,150,1)), keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'), keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'), keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'), keras.layers.Flatten(), keras.layers.Dense(8, activation="softmax") ]) # Create random image img = np.zeros([150,150,3], dtype=np.uint8) img[:,:,0] = np.ones([150,150])*64 img[:,:,1] = np.ones([150,150])*128 img[:,:,2] = np.ones([150,150])*192 g_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) g_img = np.expand_dims(g_img, axis=0) g_img = np.expand_dims(g_img, axis=-1) # (1, 150, 150, 1) prediction = model.predict(g_img) print(prediction.shape)
1.15.2 (1, 8)