Untitled

def unet_dense(input_size=(384, 384, 1), dense_size: int = 100, dropout_rate: float = 0.5, skip_connections: bool = True):


    # Build the model
    inputs = Input(input_size)


    # Contraction path
    c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(inputs)
    c1 = Dropout(0.5)(c1)
    c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)
    p1 = MaxPooling2D((2, 2))(c1)

    c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)
    c2 = Dropout(0.5)(c2)
    c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)
    p2 = MaxPooling2D((2, 2))(c2)

    c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)
    c3 = Dropout(0.5)(c3)
    c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)
    p3 = MaxPooling2D((2, 2))(c3)

    c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)
    c4 = Dropout(0.5)(c4)
    c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)
    p4 = MaxPooling2D(pool_size=(2, 2))(c4)

    c5 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)
    c5 = Dropout(0.5)(c5)
    c5 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)
    p5 = MaxPooling2D(pool_size=(2, 2))(c5)

    flatten = Flatten()(p5)
    d1 = Dense(1440, activation='relu')(flatten)
    mod = Dropout(0.5)(d1)
    bottle = Dense(dense_size, activation='sigmoid')(mod)
    d2 = Dense(1440, activation='relu')(bottle)
    reshape = Reshape((12, 12, 10))(d2)


    # Expansive path
    u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(reshape)
    u6 = concatenate([u6, c5])
    c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)
    c6 = Dropout(0.5)(c6)
    c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)

    u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
    u7 = concatenate([u7, c4])
    c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)
    c7 = Dropout(0.5)(c7)
    c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)

    u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
    u8 = concatenate([u8, c3])
    c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)
    c8 = Dropout(0.5)(c8)
    c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)

    u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
    u9 = concatenate([u9, c2], axis=3)
    c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)
    c9 = Dropout(0.5)(c9)
    c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)

    u10 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c9)
    u10 = concatenate([u10, c1], axis=3)
    c10 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u10)
    c10 = Dropout(0.5)(c10)
    c10 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c10)

    outputs = Conv2D(1, (1, 1), activation='sigmoid')(c10)

    return Model(inputs, outputs)

autoencoder = unet_dense(input_size=(384, 384, 1), dense_size=120)

autoencoder.compile(optimizer=tensorflow.keras.optimizers.Adam(learning_rate=0.0001),
                    loss="binary_crossentropy",
                    metrics=[MeanSquaredError()])
autoencoder.summary()

history = autoencoder.fit(
    x_train,
    x_train,
    epochs=400,
    batch_size=32,
    validation_data=(x_test, x_test),
)