Datasets:
HenryAI
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KerasCodeExamples.txt

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conv2d_5[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 40, 40, 128) 0 add_4[0][0]
__________________________________________________________________________________________________
conv2d_transpose_4 (Conv2DTrans (None, 40, 40, 64) 73792 activation_11[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 40, 40, 64) 256 conv2d_transpose_4[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 40, 40, 64) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
conv2d_transpose_5 (Conv2DTrans (None, 40, 40, 64) 36928 activation_12[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 40, 40, 64) 256 conv2d_transpose_5[0][0]
__________________________________________________________________________________________________
up_sampling2d_5 (UpSampling2D) (None, 80, 80, 128) 0 add_4[0][0]
__________________________________________________________________________________________________
up_sampling2d_4 (UpSampling2D) (None, 80, 80, 64) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 80, 80, 64) 8256 up_sampling2d_5[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 80, 80, 64) 0 up_sampling2d_4[0][0]
conv2d_6[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 80, 80, 64) 0 add_5[0][0]
__________________________________________________________________________________________________
conv2d_transpose_6 (Conv2DTrans (None, 80, 80, 32) 18464 activation_13[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 80, 80, 32) 128 conv2d_transpose_6[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 80, 80, 32) 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
conv2d_transpose_7 (Conv2DTrans (None, 80, 80, 32) 9248 activation_14[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 80, 80, 32) 128 conv2d_transpose_7[0][0]
__________________________________________________________________________________________________
up_sampling2d_7 (UpSampling2D) (None, 160, 160, 64) 0 add_5[0][0]
__________________________________________________________________________________________________
up_sampling2d_6 (UpSampling2D) (None, 160, 160, 32) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 160, 160, 32) 2080 up_sampling2d_7[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 160, 160, 32) 0 up_sampling2d_6[0][0]
conv2d_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 160, 160, 3) 867 add_6[0][0]
==================================================================================================
Total params: 2,058,979
Trainable params: 2,055,203
Non-trainable params: 3,776
__________________________________________________________________________________________________
Set aside a validation split
import random
# Split our img paths into a training and a validation set
val_samples = 1000
random.Random(1337).shuffle(input_img_paths)
random.Random(1337).shuffle(target_img_paths)
train_input_img_paths = input_img_paths[:-val_samples]
train_target_img_paths = target_img_paths[:-val_samples]
val_input_img_paths = input_img_paths[-val_samples:]
val_target_img_paths = target_img_paths[-val_samples:]
# Instantiate data Sequences for each split
train_gen = OxfordPets(
batch_size, img_size, train_input_img_paths, train_target_img_paths
)
val_gen = OxfordPets(batch_size, img_size, val_input_img_paths, val_target_img_paths)
Train the model
# Configure the model for training.
# We use the \"sparse\" version of categorical_crossentropy
# because our target data is integers.
model.compile(optimizer=\"rmsprop\", loss=\"sparse_categorical_crossentropy\")
callbacks = [
keras.callbacks.ModelCheckpoint(\"oxford_segmentation.h5\", save_best_only=True)
]
# Train the model, doing validation at the end of each epoch.
epochs = 15
model.fit(train_gen, epochs=epochs, validation_data=val_gen, callbacks=callbacks)
Epoch 1/15
2/199 [..............................] - ETA: 13s - loss: 5.4602WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0462s vs `on_train_batch_end` time: 0.0935s). Check your callbacks.
199/199 [==============================] - 32s 161ms/step - loss: 0.9396 - val_loss: 3.7159
Epoch 2/15
199/199 [==============================] - 32s 159ms/step - loss: 0.4911 - val_loss: 2.2709
Epoch 3/15
199/199 [==============================] - 32s 160ms/step - loss: 0.4205 - val_loss: 0.5184
Epoch 4/15
199/199 [==============================] - 32s 159ms/step - loss: 0.3739 - val_loss: 0.4584
Epoch 5/15
199/199 [==============================] - 32s 160ms/step - loss: 0.3416 - val_loss: 0.3968
Epoch 6/15
199/199 [==============================] - 32s 159ms/step - loss: 0.3131 - val_loss: 0.4059
Epoch 7/15
199/199 [==============================] - 31s 157ms/step - loss: 0.2895 - val_loss: 0.3963
Epoch 8/15
199/199 [==============================] - 31s 156ms/step - loss: 0.2695 - val_loss: 0.4035
Epoch 9/15
199/199 [==============================] - 31s 157ms/step - loss: 0.2528 - val_loss: 0.4184
Epoch 10/15