Image Segmentation with U-Net（吴恩达课程）

# UNQ_C1
# GRADED FUNCTION: conv_block
def conv_block(inputs=None, n_filters=32, dropout_prob=0, max_pooling=True):"""Convolutional downsampling blockArguments:inputs -- Input tensorn_filters -- Number of filters for the convolutional layersdropout_prob -- Dropout probabilitymax_pooling -- Use MaxPooling2D to reduce the spatial dimensions of the output volumeReturns: next_layer, skip_connection --  Next layer and skip connection outputs"""### START CODE HEREconv = Conv2D(n_filters, # Number of filters3,   # Kernel size   activation='relu',padding='same',kernel_initializer='he_normal')(inputs)conv = Conv2D(n_filters, # Number of filters3,   # Kernel sizeactivation='relu',padding='same',kernel_initializer='he_normal')(conv)### END CODE HERE# if dropout_prob > 0 add a dropout layer, with the variable dropout_prob as parameterif dropout_prob > 0:### START CODE HEREconv = Dropout(rate=dropout_prob)(conv)### END CODE HERE# if max_pooling is True add a MaxPooling2D with 2x2 pool_sizeif max_pooling:### START CODE HEREnext_layer = MaxPooling2D(2,2)(conv)### END CODE HEREelse:next_layer = convskip_connection = convreturn next_layer, skip_connection

# UNQ_C2
# GRADED FUNCTION: upsampling_block
def upsampling_block(expansive_input, contractive_input, n_filters=32):"""Convolutional upsampling blockArguments:expansive_input -- Input tensor from previous layercontractive_input -- Input tensor from previous skip layern_filters -- Number of filters for the convolutional layersReturns: conv -- Tensor output"""### START CODE HEREup = Conv2DTranspose(n_filters,    # number of filters3,    # Kernel sizestrides=2,padding='same')(expansive_input)# Merge the previous output and the contractive_inputmerge = concatenate([up, contractive_input], axis=3)conv = Conv2D(n_filters,   # Number of filters3,     # Kernel sizeactivation='relu',padding='same',kernel_initializer='he_normal')(merge)conv = Conv2D(n_filters,  # Number of filters3,   # Kernel sizeactivation='relu',padding='same',kernel_initializer='he_normal')(conv)### END CODE HEREreturn conv

# UNQ_C3
# GRADED FUNCTION: unet_model
def unet_model(input_size=(96, 128, 3), n_filters=32, n_classes=23):"""Unet modelArguments:input_size -- Input shape n_filters -- Number of filters for the convolutional layersn_classes -- Number of output classesReturns: model -- tf.keras.Model"""inputs = Input(input_size)# Contracting Path (encoding)# Add a conv_block with the inputs of the unet_ model and n_filters### START CODE HEREcblock1 = conv_block(inputs, n_filters)# Chain the first element of the output of each block to be the input of the next conv_block. # Double the number of filters at each new stepcblock2 = conv_block(cblock1[0], n_filters*2)cblock3 = conv_block(cblock2[0], n_filters*4)cblock4 = conv_block(cblock3[0], n_filters*8, dropout_prob=0.3) # Include a dropout of 0.3 for this layer# Include a dropout of 0.3 for this layer, and avoid the max_pooling layercblock5 = conv_block(cblock4[0],  n_filters*16, dropout_prob=0.3, max_pooling=False) ### END CODE HERE# Expanding Path (decoding)# Add the first upsampling_block.# Use the cblock5[0] as expansive_input and cblock4[1] as contractive_input and n_filters * 8### START CODE HEREublock6 = upsampling_block(cblock5[0], cblock4[1],  n_filters * 8)# Chain the output of the previous block as expansive_input and the corresponding contractive block output.# Note that you must use the second element of the contractive block i.e before the maxpooling layer. # At each step, use half the number of filters of the previous block ublock7 = upsampling_block(ublock6, cblock3[1], n_filters * 4)ublock8 = upsampling_block(ublock7, cblock2[1], n_filters * 2)ublock9 = upsampling_block(ublock8, cblock1[1], n_filters)### END CODE HEREconv9 = Conv2D(n_filters,3,activation='relu',padding='same',kernel_initializer='he_normal')(ublock9)# Add a Conv2D layer with n_classes filter, kernel size of 1 and a 'same' padding### START CODE HEREconv10 = Conv2D(n_classes, 1, padding='same')(conv9)### END CODE HEREmodel = tf.keras.Model(inputs=inputs, outputs=conv10)return model

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