Why does SimpleNet achieve accuracy 0.9+ but AlexNet does not?

Question

I am trying to classify some 224x224 RGB images.

I have 3 potential labels, but am currently only supplying training images for 2. My training set has approx. 2K images for both labels.

When I train with simple net I get 0.9+ accuracy within 6 epochs.

When I train with AlexNet I am still only around 0.5 accuracy after 50 epochs.

Why might this be?

def SimpleNet():
    model = keras.Sequential([
        keras.layers.Conv2D(64, kernel_size=3, activation=tf.nn.relu, input_shape=(224, 224, 3)),
        keras.layers.Dropout(0.75),
        keras.layers.Conv2D(32, kernel_size=3, activation=tf.nn.relu),
        keras.layers.Dropout(0.75),
        keras.layers.Flatten(),
        keras.layers.Dense(3, activation=tf.nn.softmax)
    ])
    return model

def AlexNet():
    model = keras.Sequential()

    # 1st Convolutional Layer
    model.add(keras.layers.Conv2D(filters=96, input_shape=(224, 224, 3), kernel_size=(11, 11), strides=(4, 4), padding='valid'))
    model.add(keras.layers.Activation('relu'))
    # Max Pooling
    model.add(keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))

    # 2nd Convolutional Layer
    model.add(keras.layers.Conv2D(filters=256, kernel_size=(11, 11), strides=(1, 1), padding='valid'))
    model.add(keras.layers.Activation('relu'))
    # Max Pooling
    model.add(keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))

    # 3rd Convolutional Layer
    model.add(keras.layers.Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), padding='valid'))
    model.add(keras.layers.Activation('relu'))

    # 4th Convolutional Layer
    model.add(keras.layers.Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), padding='valid'))
    model.add(keras.layers.Activation('relu'))

    # 5th Convolutional Layer
    model.add(keras.layers.Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='valid'))
    model.add(keras.layers.Activation('relu'))
    # Max Pooling
    model.add(keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))

    # Passing it to a Fully Connected layer
    model.add(keras.layers.Flatten())
    # 1st Fully Connected Layer
    model.add(keras.layers.Dense(4096, input_shape=(224 * 224 * 3,)))
    model.add(keras.layers.Activation('relu'))
    # Add Dropout to prevent overfitting
    model.add(keras.layers.Dropout(0.4))

    # 2nd Fully Connected Layer
    model.add(keras.layers.Dense(4096))
    model.add(keras.layers.Activation('relu'))
    # Add Dropout
    model.add(keras.layers.Dropout(0.4))

    # 3rd Fully Connected Layer
    model.add(keras.layers.Dense(1000))
    model.add(keras.layers.Activation('relu'))
    # Add Dropout
    model.add(keras.layers.Dropout(0.4))

    # Output Layer
    model.add(keras.layers.Dense(3))
    model.add(keras.layers.Activation('softmax'))

    return model