You will be building your own U-Net, a type of CNN designed for quick, precise image segmentation, and using it to predict a label for every single pixel in an image - in this case, an image from a self-driving car dataset.
This type of image classification is called semantic image segmentation. It’s similar to object detection in that both ask the question: “What objects are in this image and where in the image are those objects located?,” but where object detection labels objects with bounding boxes that may include pixels that aren’t part of the object, semantic image segmentation allows you to predict a precise mask for each object in the image by labeling each pixel in the image with its corresponding class. The word “semantic” here refers to what’s being shown, so for example the “Car” class is indicated below by the dark blue mask, and “Person” is indicated with a red mask:
As you might imagine, region-specific labeling is a pretty crucial consideration for self-driving cars, which require a pixel-perfect understanding of their environment so they can change lanes and avoid other cars, or any number of traffic obstacles that can put peoples’ lives in danger.
By the time you finish this notebook, you’ll be able to:
import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Conv2DTranspose
from tensorflow.keras.layers import concatenate
from test_utils import summary, comparatorimport os
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import imageio
import matplotlib.pyplot as plt
%matplotlib inline
path = ''
image_path = os.path.join(path, './data/CameraRGB/')
mask_path = os.path.join(path, './data/CameraMask/')
image_list_orig = os.listdir(image_path)
image_list = [image_path+i for i in image_list_orig]
mask_list = [mask_path+i for i in image_list_orig]After you are done exploring, revert back to N=2. Otherwise the autograder will throw a list index out of range error.
N = 2
img = imageio.imread(image_list[N])
mask = imageio.imread(mask_list[N])
#mask = np.array([max(mask[i, j]) for i in range(mask.shape[0]) for j in range(mask.shape[1])]).reshape(img.shape[0], img.shape[1])
fig, arr = plt.subplots(1, 2, figsize=(14, 10))
arr[0].imshow(img)
arr[0].set_title('Image')
arr[1].imshow(mask[:, :, 0])
arr[1].set_title('Segmentation')Text(0.5, 1.0, 'Segmentation')
image_list_ds = tf.data.Dataset.list_files(image_list, shuffle=False)
mask_list_ds = tf.data.Dataset.list_files(mask_list, shuffle=False)
for path in zip(image_list_ds.take(3), mask_list_ds.take(3)):
print(path)(<tf.Tensor: shape=(), dtype=string, numpy=b'./data/CameraRGB/000026.png'>, <tf.Tensor: shape=(), dtype=string, numpy=b'./data/CameraMask/000026.png'>)
(<tf.Tensor: shape=(), dtype=string, numpy=b'./data/CameraRGB/000027.png'>, <tf.Tensor: shape=(), dtype=string, numpy=b'./data/CameraMask/000027.png'>)
(<tf.Tensor: shape=(), dtype=string, numpy=b'./data/CameraRGB/000028.png'>, <tf.Tensor: shape=(), dtype=string, numpy=b'./data/CameraMask/000028.png'>)image_filenames = tf.constant(image_list)
masks_filenames = tf.constant(mask_list)
dataset = tf.data.Dataset.from_tensor_slices((image_filenames, masks_filenames))
for image, mask in dataset.take(1):
print(image)
print(mask)tf.Tensor(b'./data/CameraRGB/002128.png', shape=(), dtype=string)
tf.Tensor(b'./data/CameraMask/002128.png', shape=(), dtype=string)Normally, you normalize your image values by dividing them by 255. This sets them between 0 and 1. However, using tf.image.convert_image_dtype with tf.float32 sets them between 0 and 1 for you, so there’s no need to further divide them by 255.
def process_path(image_path, mask_path):
img = tf.io.read_file(image_path)
img = tf.image.decode_png(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
mask = tf.io.read_file(mask_path)
mask = tf.image.decode_png(mask, channels=3)
mask = tf.math.reduce_max(mask, axis=-1, keepdims=True)
return img, mask
def preprocess(image, mask):
input_image = tf.image.resize(image, (96, 128), method='nearest')
input_mask = tf.image.resize(mask, (96, 128), method='nearest')
return input_image, input_mask
image_ds = dataset.map(process_path)
processed_image_ds = image_ds.map(preprocess)U-Net, named for its U-shape, was originally created in 2015 for tumor detection, but in the years since has become a very popular choice for other semantic segmentation tasks.
U-Net builds on a previous architecture called the Fully Convolutional Network, or FCN, which replaces the dense layers found in a typical CNN with a transposed convolution layer that upsamples the feature map back to the size of the original input image, while preserving the spatial information. This is necessary because the dense layers destroy spatial information (the “where” of the image), which is an essential part of image segmentation tasks. An added bonus of using transpose convolutions is that the input size no longer needs to be fixed, as it does when dense layers are used.
Unfortunately, the final feature layer of the FCN suffers from information loss due to downsampling too much. It then becomes difficult to upsample after so much information has been lost, causing an output that looks rough.
U-Net improves on the FCN, using a somewhat similar design, but differing in some important ways. Instead of one transposed convolution at the end of the network, it uses a matching number of convolutions for downsampling the input image to a feature map, and transposed convolutions for upsampling those maps back up to the original input image size. It also adds skip connections, to retain information that would otherwise become lost during encoding. Skip connections send information to every upsampling layer in the decoder from the corresponding downsampling layer in the encoder, capturing finer information while also keeping computation low. These help prevent information loss, as well as model overfitting.
Contracting path (Encoder containing downsampling steps):
Images are first fed through several convolutional layers which reduce height and width, while growing the number of channels.
The contracting path follows a regular CNN architecture, with convolutional layers, their activations, and pooling layers to downsample the image and extract its features. In detail, it consists of the repeated application of two 3 x 3 same padding convolutions, each followed by a rectified linear unit (ReLU) and a 2 x 2 max pooling operation with stride 2 for downsampling. At each downsampling step, the number of feature channels is doubled.
Crop function: This step crops the image from the contracting path and concatenates it to the current image on the expanding path to create a skip connection.
Expanding path (Decoder containing upsampling steps):
The expanding path performs the opposite operation of the contracting path, growing the image back to its original size, while shrinking the channels gradually.
In detail, each step in the expanding path upsamples the feature map, followed by a 2 x 2 convolution (the transposed convolution). This transposed convolution halves the number of feature channels, while growing the height and width of the image.
Next is a concatenation with the correspondingly cropped feature map from the contracting path, and two 3 x 3 convolutions, each followed by a ReLU. You need to perform cropping to handle the loss of border pixels in every convolution.
Final Feature Mapping Block: In the final layer, a 1x1 convolution is used to map each 64-component feature vector to the desired number of classes. The channel dimensions from the previous layer correspond to the number of filters used, so when you use 1x1 convolutions, you can transform that dimension by choosing an appropriate number of 1x1 filters. When this idea is applied to the last layer, you can reduce the channel dimensions to have one layer per class.
The U-Net network has 23 convolutional layers in total.
The figures shown in the assignment for the U-Net architecture depict the layer dimensions and filter sizes as per the original paper on U-Net with smaller images. However, due to computational constraints for this assignment, you will code only half of those filters. The purpose of showing you the original dimensions is to give you the flavour of the original U-Net architecture. The important takeaway is that you multiply by 2 the number of filters used in the previous step. The notebook includes all of the necessary instructions and hints to help you code the U-Net architecture needed for this assignment.
The encoder is a stack of various conv_blocks:
Each conv_block() is composed of 2 Conv2D layers with ReLU activations. We will apply Dropout, and MaxPooling2D to some conv_blocks, as you will verify in the following sections, specifically to the last two blocks of the downsampling.
The function will return two tensors: - next_layer: That will go into the next block. - skip_connection: That will go into the corresponding decoding block.
Note: If max_pooling=True, the next_layer will be the output of the MaxPooling2D layer, but the skip_connection will be the output of the previously applied layer(Conv2D or Dropout, depending on the case). Else, both results will be identical.
Implement conv_block(...). Here are the instructions for each step in the conv_block, or contracting block:
n_filters filters with kernel_size set to 3, kernel_initializer set to ‘he_normal’, padding set to ‘same’ and ‘relu’ activation.dropout_prob > 0, then add a Dropout layer with parameter dropout_probmax_pooling is set to True, then add a MaxPooling2D layer with 2x2 pool sizedef conv_block(inputs=None, n_filters=32, dropout_prob=0, max_pooling=True):
"""
Convolutional downsampling block
Arguments:
inputs -- Input tensor
n_filters -- Number of filters for the convolutional layers
dropout_prob -- Dropout probability
max_pooling -- Use MaxPooling2D to reduce the spatial dimensions of the output volume
Returns:
next_layer, skip_connection -- Next layer and skip connection outputs
"""
conv = Conv2D(n_filters, # Number of filters
3, # Kernel size
activation='relu',
padding='same',
kernel_initializer='he_normal')(inputs)
conv = Conv2D(n_filters, # Number of filters
3, # Kernel size
activation='relu',
padding='same',
# set 'kernel_initializer' same as above
kernel_initializer='he_normal')(conv)
# if dropout_prob > 0 add a dropout layer, with the variable dropout_prob as parameter
if dropout_prob > 0:
### START CODE HERE
conv = Dropout(dropout_prob)(conv)
# if max_pooling is True add a MaxPooling2D with 2x2 pool_size
if max_pooling:
next_layer = MaxPooling2D(2, strides=2)(conv)
else:
next_layer = conv
skip_connection = conv
return next_layer, skip_connectioninput_size=(96, 128, 3)
n_filters = 32
inputs = Input(input_size)
cblock1 = conv_block(inputs, n_filters * 1)
model1 = tf.keras.Model(inputs=inputs, outputs=cblock1)
output1 = [['InputLayer', [(None, 96, 128, 3)], 0],
['Conv2D', (None, 96, 128, 32), 896, 'same', 'relu', 'HeNormal'],
['Conv2D', (None, 96, 128, 32), 9248, 'same', 'relu', 'HeNormal'],
['MaxPooling2D', (None, 48, 64, 32), 0, (2, 2)]]
print('Block 1:')
for layer in summary(model1):
print(layer)
comparator(summary(model1), output1)
inputs = Input(input_size)
cblock1 = conv_block(inputs, n_filters * 32, dropout_prob=0.1, max_pooling=True)
model2 = tf.keras.Model(inputs=inputs, outputs=cblock1)
output2 = [['InputLayer', [(None, 96, 128, 3)], 0],
['Conv2D', (None, 96, 128, 1024), 28672, 'same', 'relu', 'HeNormal'],
['Conv2D', (None, 96, 128, 1024), 9438208, 'same', 'relu', 'HeNormal'],
['Dropout', (None, 96, 128, 1024), 0, 0.1],
['MaxPooling2D', (None, 48, 64, 1024), 0, (2, 2)]]
print('\nBlock 2:')
for layer in summary(model2):
print(layer)
comparator(summary(model2), output2)Block 1:
['InputLayer', [(None, 96, 128, 3)], 0]
['Conv2D', (None, 96, 128, 32), 896, 'same', 'relu', 'HeNormal']
['Conv2D', (None, 96, 128, 32), 9248, 'same', 'relu', 'HeNormal']
['MaxPooling2D', (None, 48, 64, 32), 0, (2, 2)]
All tests passed!
Block 2:
['InputLayer', [(None, 96, 128, 3)], 0]
['Conv2D', (None, 96, 128, 1024), 28672, 'same', 'relu', 'HeNormal']
['Conv2D', (None, 96, 128, 1024), 9438208, 'same', 'relu', 'HeNormal']
['Dropout', (None, 96, 128, 1024), 0, 0.1]
['MaxPooling2D', (None, 48, 64, 1024), 0, (2, 2)]
All tests passed!The decoder, or upsampling block, upsamples the features back to the original image size. At each upsampling level, you’ll take the output of the corresponding encoder block and concatenate it before feeding to the next decoder block.
There are two new components in the decoder: up and merge. These are the transpose convolution and the skip connections. In addition, there are two more convolutional layers set to the same parameters as in the encoder.
Here you’ll encounter the Conv2DTranspose layer, which performs the inverse of the Conv2D layer. You can read more about it here.
Implement upsampling_block(...).
For the function upsampling_block: * Takes the arguments expansive_input (which is the input tensor from the previous layer) and contractive_input (the input tensor from the previous skip layer) * The number of filters here is the same as in the downsampling block you completed previously * Your Conv2DTranspose layer will take n_filters with shape (3,3) and a stride of (2,2), with padding set to same. It’s applied to expansive_input, or the input tensor from the previous layer.
This block is also where you’ll concatenate the outputs from the encoder blocks, creating skip connections.
axis of 3. In general, you can concatenate the tensors in the order that you prefer. But for the grader, it is important that you use [up, contractive_input]For the final component, set the parameters for two Conv2D layers to the same values that you set for the two Conv2D layers in the encoder (ReLU activation, He normal initializer, same padding).
def upsampling_block(expansive_input, contractive_input, n_filters=32):
"""
Convolutional upsampling block
Arguments:
expansive_input -- Input tensor from previous layer
contractive_input -- Input tensor from previous skip layer
n_filters -- Number of filters for the convolutional layers
Returns:
conv -- Tensor output
"""
up = Conv2DTranspose(
n_filters, # number of filters
3, # Kernel size
strides=2,
padding='same')(expansive_input)
# Merge the previous output and the contractive_input
merge = concatenate([up, contractive_input], axis=3)
conv = Conv2D(n_filters, # Number of filters
3, # Kernel size
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge)
conv = Conv2D(n_filters, # Number of filters
3, # Kernel size
activation='relu',
padding='same',
# set 'kernel_initializer' same as above
kernel_initializer='he_normal')(conv)
return convinput_size1=(12, 16, 256)
input_size2 = (24, 32, 128)
n_filters = 32
expansive_inputs = Input(input_size1)
contractive_inputs = Input(input_size2)
cblock1 = upsampling_block(expansive_inputs, contractive_inputs, n_filters * 1)
model1 = tf.keras.Model(inputs=[expansive_inputs, contractive_inputs], outputs=cblock1)
output1 = [['InputLayer', [(None, 12, 16, 256)], 0],
['Conv2DTranspose', (None, 24, 32, 32), 73760],
['InputLayer', [(None, 24, 32, 128)], 0],
['Concatenate', (None, 24, 32, 160), 0],
['Conv2D', (None, 24, 32, 32), 46112, 'same', 'relu', 'HeNormal'],
['Conv2D', (None, 24, 32, 32), 9248, 'same', 'relu', 'HeNormal']]
print('Block 1:')
for layer in summary(model1):
print(layer)
comparator(summary(model1), output1)Block 1:
['InputLayer', [(None, 12, 16, 256)], 0]
['Conv2DTranspose', (None, 24, 32, 32), 73760]
['InputLayer', [(None, 24, 32, 128)], 0]
['Concatenate', (None, 24, 32, 160), 0]
['Conv2D', (None, 24, 32, 32), 46112, 'same', 'relu', 'HeNormal']
['Conv2D', (None, 24, 32, 32), 9248, 'same', 'relu', 'HeNormal']
All tests passed!This is where you’ll put it all together, by chaining the encoder, bottleneck, and decoder! You’ll need to specify the number of output channels, which for this particular set would be 23. That’s because there are 23 possible labels for each pixel in this self-driving car dataset.
For the function unet_model, specify the input shape, number of filters, and number of classes (23 in this case).
For the first half of the model:
conv_block4, add dropout_prob of 0.3dropout_prob to 0.3 again, and turn off max poolingFor the second half:
n_filters * 8. This is your bottleneck layer.conv9 is a Conv2D layer with ReLU activation, He normal initializer, same paddingconv10 is a Conv2D that takes the number of classes as the filter, a kernel size of 1, and “same” padding. The output of conv10 is the output of your 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(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 step
cblock2 = 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=.3) # Include a dropout_prob of 0.3 for this layer
# Include a dropout_prob of 0.3 for this layer, and avoid the max_pooling layer
cblock5 = conv_block(cblock4[0], n_filters=n_filters*16, dropout_prob=.3, max_pooling=None)
# 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 HERE
ublock6 = 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*1)
conv9 = Conv2D(n_filters,
3,
activation='relu',
padding='same',
# set 'kernel_initializer' same as above exercises
kernel_initializer='he_normal')(ublock9)
# Add a Conv2D layer with n_classes filter, kernel size of 1 and a 'same' padding
conv10 = Conv2D(n_classes, 1, padding='same')(conv9)
model = tf.keras.Model(inputs=inputs, outputs=conv10)
return modelimport outputs
img_height = 96
img_width = 128
num_channels = 3
unet = unet_model((img_height, img_width, num_channels))
comparator(summary(unet), outputs.unet_model_output)All tests passed!img_height = 96
img_width = 128
num_channels = 3
unet = unet_model((img_height, img_width, num_channels))unet.summary()Model: "model_4"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_6 (InputLayer) [(None, 96, 128, 3) 0 []
]
conv2d_26 (Conv2D) (None, 96, 128, 32) 896 ['input_6[0][0]']
conv2d_27 (Conv2D) (None, 96, 128, 32) 9248 ['conv2d_26[0][0]']
max_pooling2d_6 (MaxPooling2D) (None, 48, 64, 32) 0 ['conv2d_27[0][0]']
conv2d_28 (Conv2D) (None, 48, 64, 64) 18496 ['max_pooling2d_6[0][0]']
conv2d_29 (Conv2D) (None, 48, 64, 64) 36928 ['conv2d_28[0][0]']
max_pooling2d_7 (MaxPooling2D) (None, 24, 32, 64) 0 ['conv2d_29[0][0]']
conv2d_30 (Conv2D) (None, 24, 32, 128) 73856 ['max_pooling2d_7[0][0]']
conv2d_31 (Conv2D) (None, 24, 32, 128) 147584 ['conv2d_30[0][0]']
max_pooling2d_8 (MaxPooling2D) (None, 12, 16, 128) 0 ['conv2d_31[0][0]']
conv2d_32 (Conv2D) (None, 12, 16, 256) 295168 ['max_pooling2d_8[0][0]']
conv2d_33 (Conv2D) (None, 12, 16, 256) 590080 ['conv2d_32[0][0]']
dropout_3 (Dropout) (None, 12, 16, 256) 0 ['conv2d_33[0][0]']
max_pooling2d_9 (MaxPooling2D) (None, 6, 8, 256) 0 ['dropout_3[0][0]']
conv2d_34 (Conv2D) (None, 6, 8, 512) 1180160 ['max_pooling2d_9[0][0]']
conv2d_35 (Conv2D) (None, 6, 8, 512) 2359808 ['conv2d_34[0][0]']
dropout_4 (Dropout) (None, 6, 8, 512) 0 ['conv2d_35[0][0]']
conv2d_transpose_5 (Conv2DTran (None, 12, 16, 256) 1179904 ['dropout_4[0][0]']
spose)
concatenate_5 (Concatenate) (None, 12, 16, 512) 0 ['conv2d_transpose_5[0][0]',
'dropout_3[0][0]']
conv2d_36 (Conv2D) (None, 12, 16, 256) 1179904 ['concatenate_5[0][0]']
conv2d_37 (Conv2D) (None, 12, 16, 256) 590080 ['conv2d_36[0][0]']
conv2d_transpose_6 (Conv2DTran (None, 24, 32, 128) 295040 ['conv2d_37[0][0]']
spose)
concatenate_6 (Concatenate) (None, 24, 32, 256) 0 ['conv2d_transpose_6[0][0]',
'conv2d_31[0][0]']
conv2d_38 (Conv2D) (None, 24, 32, 128) 295040 ['concatenate_6[0][0]']
conv2d_39 (Conv2D) (None, 24, 32, 128) 147584 ['conv2d_38[0][0]']
conv2d_transpose_7 (Conv2DTran (None, 48, 64, 64) 73792 ['conv2d_39[0][0]']
spose)
concatenate_7 (Concatenate) (None, 48, 64, 128) 0 ['conv2d_transpose_7[0][0]',
'conv2d_29[0][0]']
conv2d_40 (Conv2D) (None, 48, 64, 64) 73792 ['concatenate_7[0][0]']
conv2d_41 (Conv2D) (None, 48, 64, 64) 36928 ['conv2d_40[0][0]']
conv2d_transpose_8 (Conv2DTran (None, 96, 128, 32) 18464 ['conv2d_41[0][0]']
spose)
concatenate_8 (Concatenate) (None, 96, 128, 64) 0 ['conv2d_transpose_8[0][0]',
'conv2d_27[0][0]']
conv2d_42 (Conv2D) (None, 96, 128, 32) 18464 ['concatenate_8[0][0]']
conv2d_43 (Conv2D) (None, 96, 128, 32) 9248 ['conv2d_42[0][0]']
conv2d_44 (Conv2D) (None, 96, 128, 32) 9248 ['conv2d_43[0][0]']
conv2d_45 (Conv2D) (None, 96, 128, 23) 759 ['conv2d_44[0][0]']
==================================================================================================
Total params: 8,640,471
Trainable params: 8,640,471
Non-trainable params: 0
__________________________________________________________________________________________________In semantic segmentation, you need as many masks as you have object classes. In the dataset you’re using, each pixel in every mask has been assigned a single integer probability that it belongs to a certain class, from 0 to num_classes-1. The correct class is the layer with the higher probability.
This is different from categorical crossentropy, where the labels should be one-hot encoded (just 0s and 1s). Here, you’ll use sparse categorical crossentropy as your loss function, to perform pixel-wise multiclass prediction. Sparse categorical crossentropy is more efficient than other loss functions when you’re dealing with lots of classes.
unet.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])Below, define a function that allows you to display both an input image, and its ground truth: the true mask. The true mask is what your trained model output is aiming to get as close to as possible.
def display(display_list):
plt.figure(figsize=(15, 15))
title = ['Input Image', 'True Mask', 'Predicted Mask']
for i in range(len(display_list)):
plt.subplot(1, len(display_list), i+1)
plt.title(title[i])
plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
plt.axis('off')
plt.show()for image, mask in image_ds.take(1):
sample_image, sample_mask = image, mask
print(mask.shape)
display([sample_image, sample_mask])(480, 640, 1)
for image, mask in processed_image_ds.take(1):
sample_image, sample_mask = image, mask
print(mask.shape)
display([sample_image, sample_mask])(96, 128, 1)
EPOCHS = 5
VAL_SUBSPLITS = 5
BUFFER_SIZE = 500
BATCH_SIZE = 32
train_dataset = processed_image_ds.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
print(processed_image_ds.element_spec)
model_history = unet.fit(train_dataset, epochs=EPOCHS)(TensorSpec(shape=(96, 128, 3), dtype=tf.float32, name=None), TensorSpec(shape=(96, 128, 1), dtype=tf.uint8, name=None))
Epoch 1/5
34/34 [==============================] - 18s 185ms/step - loss: 1.9178 - accuracy: 0.4890
Epoch 2/5
34/34 [==============================] - 2s 47ms/step - loss: 0.7820 - accuracy: 0.7905
Epoch 3/5
34/34 [==============================] - 2s 47ms/step - loss: 0.5898 - accuracy: 0.8217
Epoch 4/5
34/34 [==============================] - 2s 47ms/step - loss: 0.4749 - accuracy: 0.8553
Epoch 5/5
34/34 [==============================] - 2s 47ms/step - loss: 0.4279 - accuracy: 0.8722Now, define a function that uses tf.argmax in the axis of the number of classes to return the index with the largest value and merge the prediction into a single image:
def create_mask(pred_mask):
pred_mask = tf.argmax(pred_mask, axis=-1)
pred_mask = pred_mask[..., tf.newaxis]
return pred_mask[0]Let’s see how your model did!
plt.plot(model_history.history["accuracy"])
Next, check your predicted masks against the true mask and the original input image:
def show_predictions(dataset=None, num=1):
"""
Displays the first image of each of the num batches
"""
if dataset:
for image, mask in dataset.take(num):
pred_mask = unet.predict(image)
display([image[0], mask[0], create_mask(pred_mask)])
else:
display([sample_image, sample_mask,
create_mask(unet.predict(sample_image[tf.newaxis, ...]))])show_predictions(train_dataset, 6)1/1 [==============================] - 0s 212ms/step
1/1 [==============================] - 0s 21ms/step
1/1 [==============================] - 0s 20ms/step
1/1 [==============================] - 0s 20ms/step
1/1 [==============================] - 0s 20ms/step
1/1 [==============================] - 0s 19ms/step
With 40 epochs you get amazing results!
You’ve come to the end of this assignment. Awesome work creating a state-of-the art model for semantic image segmentation! This is a very important task for self-driving cars to get right. Elon Musk will surely be knocking down your door at any moment. ;)
What you should remember:
https://www.coursera.org/learn/convolutional-neural-networks/