Zero loss and validation loss in Keras CNN model

[Shaoying Neo]

Hello, I am attempting to build a (simple?) crowd estimation model that classifies the images into three different broad categories depending on how many people there are in the images. 1200 images are used for training, with 20% of it used for validation. I used sentdex's tutorial on Youtube as reference to load the image data into the model; I load the images as a zip file, extract it and categorise them based on the folders they are in.

My issue is that whenever I attempt to train the model, I noticed that the loss and validation loss is always 0, which has resulted in the model not exactly training and the validation accuracy remaining the same throughout, as seen below:

How can I get the loss to actually change? Is there something I am doing wrong in terms of implementation?

So far, what I have attempted is: 

1. I tried to add a third convolutional layer, with little results.  
2. I have also tried to change the last Dense layer to model.add(Dense(3)), but I got an error saying "Shapes (None, 1) and (None, 3) are incompatible"
3. I tried using a lower learning rate (0.001?), but the model ended up returning a 0 for validation accuracy
4. Changing the optimizer did not seem to generate any changes for me

Below is a snippet of my code so far showing my model attempt:

```python

import keras.backend as K

logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")

tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)

X = X/255.0

model = Sequential()

model.add(Conv2D(64, (3,3), input_shape = X.shape[1:])) #[1:] to skip the -1

model.add(Activation("relu"))

model.add(Conv2D(64, (3,3), input_shape = X.shape[1:])) #[1:] to skip the -1

model.add(Activation("relu"))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(128, (3,3)))

model.add(Activation('relu'))

model.add(Conv2D(128, (3,3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Dropout(0.2))

model.add(Flatten()) 

model.add(Dense(128))

model.add(Activation('relu'))

#fully connected layer 

model.add(Dense(1))

model.add(Activation('softmax'))

opt = keras.optimizers.Adam(lr=0.01)

model.compile(loss='categorical_crossentropy', 

              optimizer = opt,

              metrics=['accuracy']) 

model.fit(x_train, y_train, batch_size = 100, epochs = 30, validation_data = (x_val, y_val),  callbacks=[tensorboard_callback], shuffle=True)

```

The full code can be found on Colab here! 

I'm still very new to Keras, so any pointers in the right direction would be greatly appreciated 😭

[Lance Norskog]

This has one binary output, so it should be 'binary_crossentropy' not 'categorical_crossentropy'.

You do not need the input_shape in the second Conv2D. 

--
You received this message because you are subscribed to the Google Groups "Keras-users" group.
To unsubscribe from this group and stop receiving emails from it, send an email to keras-users...@googlegroups.com.
To view this discussion on the web, visit https://groups.google.com/d/msgid/keras-users/434fb1c2-29a3-45dd-886f-991a579bd1c7n%40googlegroups.com.

--

Lance Norskog
lance....@gmail.com
Redwood City, CA

[Shaoying Neo]

Hi Lance,

Thank you so much for the reply! I made adjustments according to your advice and I was able to get numerical values for loss now!
Thank you so much :D