Hi reinoudbosch, yes issue resolved and in this assignment i strucked with onother lines of code which i will mention now so request you to help me in this
GRADED FUNCTION: unet_model
def unet_model(input_size=(96, 128, 3), n_filters=32, n_classes=23):
"""
Unet model
Arguments:
input_size -- Input shape
n_filters -- Number of filters for the convolutional layers
n_classes -- Number of output classes
Returns:
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
# cblock1 = conv_block(None, None)
# 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 step
# cblock2 = conv_block(None, None)
# cblock3 = conv_block(None, None)
# cblock4 = conv_block(None, None, dropout=None) # Include a dropout of 0.3 for this layer
# Include a dropout of 0.3 for this layer, and avoid the max_pooling layer
# cblock5 = conv_block(None, None, dropout=None, max_pooling=None)
# YOUR CODE STARTS HERE
cblock1 = conv_block(inputs, n_filters * 1)
cblock2 = conv_block(cblock1,n_filters*2)
cblock3 = conv_block(cblock2,n_filters*4)
cblock4 = conv_block(cblock3,n_filters*8,dropout_prob=0.3)
cblock5 = conv_block(cblock4,n_filters*16,dropout_prob=0.3,max_pooling=False)
# YOUR CODE ENDS 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
# ublock6 = upsampling_block(None, None, None)
# 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(None, None, None)
# ublock8 = upsampling_block(None, None, None)
# ublock9 = upsampling_block(None, None, None)
# YOUR CODE STARTS HERE
ublock6 = upsampling_block(cblock5[0],cblock4[1],n_filters * 8)
ublock7 = upsampling_block(ublock6[0],cblock3[1],n_filters * 4)
ublock8 = upsampling_block(ublock7[0],cblock2[1],n_filters*2)
ublock9 = upsampling_block(ublock8[0],cblock1[1],n_filters*1)
# YOUR CODE ENDS HERE*
conv9 = 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
# conv10 = Conv2D(None, None, padding=None)(conv9)
# YOUR CODE STARTS HERE
conv10 = Conv2D(n_classes ,kernel_size=1, padding='same')(conv9)
# YOUR CODE ENDS HERE
model = tf.keras.Model(inputs=inputs, outputs=conv10)
return model
these line of code i have written but not output its showing the following error
ValueError: Layer conv2d_8 expects 1 inputs, but it received 2 input tensors. Inputs received: [<tf.Tensor ‘max_pooling2d_2/MaxPool:0’ shape=(None, 48, 64, 32) dtype=float32>, <tf.Tensor ‘conv2d_7/Relu:0’ shape=(None, 96, 128, 32) dtype=float32>]