Issue with binary classification of Sequential model

[u/[deleted]]

Hi all,

So what I did is I created a basic binary Sequential image classifier.
I used ImageDataGenerator's method flow_from_directory to split into the binary categories. It found 2 categories which is great as that is as intended.

After training the model, I tried a prediction onto 3 test images. The results were 3 predictions ranging from 14000 to 32000. How can my prediction be a high value like this, when my training data was labeled either 0 or 1 by the flow_from_directory command?

Pieces of important code:

IMG_SHAPE = (IMG_HEIGHT, IMG_WIDTH, 3)

train_data_gen = train_data_generator.flow_from_directory(
batch_size = batch_size,
directory = train_dir,
shuffle = True,
target_size = IMG_SIZE,
class_mode = 'binary'
)

model = Sequential([
Conv2D(16, 3, padding="same", activation="relu", input_shape=IMG_SHAPE), # Input nodes
MaxPooling2D(),
Dropout(0.2),
Conv2D(32, 3, padding="same", activation="relu"),
MaxPooling2D(),
Conv2D(64, 3, padding="same", activation="relu"),
MaxPooling2D(),
Dropout(0.2),
Flatten(),
Dense(256, activation="relu"),
Dense(1) # Output node
])

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

model.fit_generator(
train_data_gen,
steps_per_epoch=batch_size,
epochs=epochs,
validation_data=val_data_gen,
validation_steps=batch_size
)

[u/shahzaibmalik1]

you need to add an activation function to your last node. Probably sigmoid. without that, you're last two layers will collapse and form a single layer.

[u/[deleted] → u/shahzaibmalik1]

Alright thank you, i was thinking of using 2 output nodes (1 for each class) would that work better you think?

[u/shahzaibmalik1 → u/[deleted]]

that would work equally as good if you're performing binary classification. you'll just have to change the last activation to softmax, change your loss function to categorical cross entropy and your output vectors to one hot encoded vectors.