[FIXED] How to fix no validation accuracy?

Issue

I'm working on a Neural Network and I've been training it recently, and it has approximately 93% accuracy on the training data and 0% accuracy on the validation data. My first thought was overfitting, but the model doesn't save in between training and I get these results in the first Epoch. I'm using keras in python with the following model code:

model = Sequential(
    [
        Conv1D(320, 8, input_shape=(560, 560), activation="relu"),
        # Conv1D(320, 8, activation="relu"),
        # Conv1D(320, 8, activation="relu"),
        # Dense(750, activation="relu"),
        # Dropout(0.6),
        Dense(1500, activation="relu"),
        Dropout(0.6),
        Dense(750, activation="relu"),
        Dropout(0.6),
        GlobalMaxPooling1D(keepdims=True),
        Dense(1, activation='softmax')
    ]
)

model.compile(optimizer=Adam(learning_rate=0.00001), loss="binary_crossentropy", metrics=['accuracy'])
earlystopping = callbacks.EarlyStopping(monitor="val_accuracy",
                                        mode="max", patience=2,
                                        restore_best_weights=True)
model1 = model.fit(x=training_x, y=training_y, batch_size=150, epochs=5, shuffle=True, verbose=1, callbacks=[earlystopping], validation_data=(val_x, val_y))

The results I'm getting look like this:

Epoch 1/5 167/167 [==============================] - 1266s 8s/step - loss: 6.4154 - accuracy: 0.9262 - val_loss: 0.0054 - val_accuracy: 0.0000e+00

I've tried changing almost all of the hyperparameters and changing the model's architecture but I keep getting similar results. Does this have anything to do with the data? The data I'm using is a 3d NumPy array containing pixel data from a bunch of images. Any help here would be greatly appreciated.

Solution

You need to use activation='sigmoid' and optimizers.RMSprop(lr=1e-4) for a binary classification.

Answered By - Jean-Marc Volle