I've been on this for the past few days and couldn't figure it out. Posted on various groups, StackOverflow etc and got suggestions from many users. I implemented these suggestions into the code shown below, but still having the same issue. Sorry for the lengthy post, but I want to be as clear as possible. All relevant code snippets are shown below:

Setting up image paths:

imagepaths = []

for root, dirs, files in os.walk(".", topdown=False): 
  for name in files:
    path = os.path.join(root, name)
    if path.endswith("jpg"): # We want only the images

Loading into arrays, preprocessing:

X = [] # Image data
y = [] # Labels

datagen = ImageDataGenerator(rescale=1./255, samplewise_center=True)

# Loops through imagepaths to load images and labels into arrays
for path in imagepaths:
  img = cv2.imread(path) # Reads image and returns np.array
  img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Converts into the corret colorspace (GRAY) #find rgb 
  img = cv2.resize(img, (75, 75)) # Reduce image size so training can be faster
  img = image.img_to_array(img)
  img = datagen.standardize(img)

  # Processing label in image path
  category = path.split("\\")[1]
  split = (category.split("_"))     
  if int(split[0]) == 0:
    label = int(split[1])
    label = int(split[0])

# Turn X and y into np.array to speed up train_test_split

X = np.array(X, dtype="float32") #ORIGINAL uint8
X = X.reshape(len(imagepaths), 75, 75, 1) 
y = np.array(y)
tf.keras.utils.to_categorical(X, num_classes=None, dtype="float32")
tf.keras.utils.to_categorical(y, num_classes=None, dtype="float32")

Creating test set:

ts = 0.3 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=ts, random_state=42)

Creating model. Yes I know its super small, just 1 layer, but I was suggested to cut down to start from the base and build up. Originally it was 5 layers, but the results are still the same.

model = Sequential()

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(75, 75, 1))) 
model.add(MaxPooling2D((2, 2)))

model.add(Dense(128, activation='relu'))
model.add(Dense(26, activation='softmax'))

Compiling the model. And fitting. I was told that the gradient could be exploding, so was suggested to add the first line with the clipnorm..

adam = keras.optimizers.Adam(clipnorm=1.)

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])  

model.fit(X_train, y_train, epochs=5, batch_size=32, verbose=1, validation_data=(X_test, y_test)) 

The final training can be seen here. The issues are, losses are NAN and accuracies are 0.

Train on 54600 samples, validate on 23400 samples
Epoch 1/5
54600/54600 [==============================] - 14s 265us/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 2/5
54600/54600 [==============================] - 15s 269us/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 3/5
54600/54600 [==============================] - 15s 273us/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 4/5
54600/54600 [==============================] - 15s 267us/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 5/5
54600/54600 [==============================] - 14s 263us/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00

Here are a list of things which I did wrong and was suggested to do by others:

I didn't standardize the data originally - So, I did ImageDataGenerator, rescaled, and standardized it.

I was suggested to turn the data to categorical, which I did use the to_categorical function (I think i did that right) but I'm not sure if there's anything else required.

Reduce model complexity. I did that brought it to only one layer to debug.

Possible exploding gradient - so changed the adam optimizer with clipnorm = 1

BACKGROUND: This model trains and recognizes the 26 letters of the alphabet. I know the dataset is fine because when I use it to train a model for 10 letters at a time (A-J) for example it works fine. The issue is only when I go from 10-26. Yes, I did try to change the dense to 26 on the original code but that did not work.

I've been staring at this and trying everything for the past two days...



2 Answers 2


I think there might be a number of errors here:

  1. There is no need to convert X to categorical, only your labels. Hence, there is not need for this line: tf.keras.utils.to_categorical(X, num_classes=None, dtype="float32")
  2. to_categorical is not inplace so you would need to reassign it to y. Change it to: y = tf.keras.utils.to_categorical(y)
  3. Once you convert them to categorical, your loss function should be 'categorical_crossentropy'

First of all I would suggest you to use datagen.flow_from_directory to load the dataset. Also your model has become too simple now, try adding atleast 1or2 more Conv layers.

For your problem, it might be a case of exploding gradient. Try reducing learning rate and adding batch normalization between layers. Maybe try SGD with momentum or RMSprop optimizer, that helps sometimes.


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