ValueError: Error when checking target: expected dense_2 to have shape (None, 2) but got array with shape (1, 1)

Hi I am new in Keras with back-end tensorflow. I have build my training and validation set of images of two possible classes; my network has to end with two classes yes or not. I have used ImageDatagenerator to read the images from the folders and prepare the training and the validation set. At the end I get the issue described in the Title. My guess is that the ImageDatagenerator is not preparing the data as I would like; Any body can explain to me how to solve it, here is the code (THANKS):

# Data Preparation
# dimensions of our images.
img_width, img_height = 256, 256

#top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 2
nb_validation_samples = 2
epochs = 50
batch_size = 1
num_classes = 2

# prepare data augmentation configuration
train_datagen = ImageDataGenerator(
                                   rescale=1. / 255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   data_format=K.image_data_format(),
                                   horizontal_flip=True)

test_datagen = ImageDataGenerator(
                                  rescale=1. / 255,
                                  data_format=K.image_data_format())

train_generator = train_datagen.flow_from_directory(
                                                    train_data_dir,
                                                    target_size=(img_height, img_width),
                                                    batch_size=batch_size,
                                                    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
                                                        validation_data_dir,
                                                        target_size=(img_height, img_width),
                                                        batch_size=batch_size,
                                                        class_mode='binary')

# create the CNN model
model = Sequential()
model.add(Conv2D(24, kernel_size=(20, 20), strides=(2,2), padding='valid', activation='relu', input_shape=(256,256,3)))

model.add(MaxPooling2D(pool_size=(7, 7), strides=(2,2), padding='valid'))

# Avoiding overfitting
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(64, activation='relu'))

# Avoiding overfitting
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

print(model.summary())

# Compile model
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['accuracy'])

# Fit the model
model.fit_generator(
                    train_generator,
                    steps_per_epoch=nb_train_samples // batch_size,
                    epochs=epochs,
                    validation_data=validation_generator,
                    validation_steps=nb_validation_samples // batch_size,
                    verbose=0)

# Save the weights
model.save_weights('first_try.h5')