import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt
import cv2
import imghdr
import os/Users/ahmed/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020
warnings.warn(
data_dir = "data"
image_exts = ['jpg', 'jpeg', 'JPG', 'JPEG', 'bmp', 'png', 'PNG']
image_class_count = 0
for image_class in os.listdir(data_dir):
if image_class.startswith('.'):
continue
image_class_count += 1
for image in os.listdir(os.path.join(data_dir, image_class)):
if image_class.startswith('.'):
continue
image_path = os.path.join(data_dir, image_class, image)
try:
img = cv2.imread(image_path)
ext = imghdr.what(image_path)
if ext not in image_exts:
print('Image {} extension not supported {}', format(image_path), ext)
os.remove(image_path)
except Exception as e:
print("Error loading {}", format(image_path))Image {} extension not supported {} data/cat/black-cat-back.jpg None
Image {} extension not supported {} data/cat/pallas-cat-1-250-384.webp webp
Image {} extension not supported {} data/cat/two-different-breeds-of-cats-side-by-side-outdoors-in-the-garden.jpg None
Image {} extension not supported {} data/cat/Orange-colored-cat-yawns-displaying-teeth.jpg None
Image {} extension not supported {} data/cat/vector.svg None
Image {} extension not supported {} data/cat/vector9.svg None
Image {} extension not supported {} data/cat/vector8.svg None
Image {} extension not supported {} data/cat/800px-Juvenile_Ragdoll.jpg None
Image {} extension not supported {} data/cat/studio-portrait-sitting-tabby-cat-600nw-2269389471.jpg None
Image {} extension not supported {} data/cat/1200px-Cat_November_2010-1a.jpg None
Image {} extension not supported {} data/cat/vector3.svg None
Image {} extension not supported {} data/cat/Kitten.jpg.webp webp
Image {} extension not supported {} data/cat/vector2.svg None
Image {} extension not supported {} data/cat/vector6.svg None
Image {} extension not supported {} data/cat/vector7.svg None
Image {} extension not supported {} data/cat/vector5.svg None
Image {} extension not supported {} data/cat/vector4.svg None
Image {} extension not supported {} data/cat/629a868d4bf2b530bba4e756_Dimensions-Animals-Cats-Intro-Group-2.svg None
Image {} extension not supported {} data/cat/Scottish-fold-cat.jpg None
Image {} extension not supported {} data/cat/unnamed-4.jpg None
Image {} extension not supported {} data/cat/vector10.svg None
Image {} extension not supported {} data/cat/vector11.svg None
Image {} extension not supported {} data/cat/Portrait-of-a-cat-with-whiskers-visible.jpg None
Image {} extension not supported {} data/dog/shutterstock_56508520-1.jpg None
Image {} extension not supported {} data/dog/vector.svg None
Image {} extension not supported {} data/dog/0e2bca7f082d4e959f38113d2129b841.jpg None
Image {} extension not supported {} data/dog/vector9.svg None
Image {} extension not supported {} data/dog/vector8.svg None
Image {} extension not supported {} data/dog/GoldenRetrieversdanspetcare.jpeg None
Image {} extension not supported {} data/dog/GettyImages-764782285-e1688762016649.jpg webp
Image {} extension not supported {} data/dog/a-small-puppy-for-a-walk-two-golden-retriever-dogs-on-the-road-in-2252922629.jpg None
Image {} extension not supported {} data/dog/purebred-adult-dog-outdoors-nature-260nw-582849898.jpg None
Image {} extension not supported {} data/dog/vector3.svg None
Image {} extension not supported {} data/dog/vector2.svg None
Image {} extension not supported {} data/dog/vector6.svg None
Image {} extension not supported {} data/dog/vector7.svg None
Image {} extension not supported {} data/dog/When-Do-Dogs-Stop-Growing-LS-long.jpg None
Image {} extension not supported {} data/dog/7db1503d8f6e4e3db43eb14ee92a8066.jpg None
Image {} extension not supported {} data/dog/vector5.svg None
Image {} extension not supported {} data/dog/Adult-dog-exercising-outdoors_resized.jpg None
Image {} extension not supported {} data/dog/vector4.svg None
libpng warning: iCCP: known incorrect sRGB profile
Image {} extension not supported {} data/dog/The_Wildest_Editorial_Adopting-Adult-Rescue-Dog_AdobeStock_303804010.jpg None
libpng warning: iCCP: known incorrect sRGB profile
Image {} extension not supported {} data/chicken/800px-Pollito.jpg None
Image {} extension not supported {} data/chicken/rooster-Rhode-Island-Red-roosters-chicken-domestication.jpg None
Image {} extension not supported {} data/chicken/.DS_Store None
Image {} extension not supported {} data/chicken/chicken2.jpg None
Image {} extension not supported {} data/chicken/clustering-chickens-henhouse-England-Devon-Members-flocks.jpg None
Image {} extension not supported {} data/chicken/img-730x400-community-content-vet-33-627239.jpg None
Image {} extension not supported {} data/chicken/Chicken.jpg None
Image {} extension not supported {} data/chicken/chicken-sunset-600nw-752120836.jpg None
Image {} extension not supported {} data/chicken/chickens-in-grass_0.jpg None
Image {} extension not supported {} data/chicken/1462061.webp webp
Image {} extension not supported {} data/chicken/colonel-by-erin-1.jpg None
libpng warning: iCCP: known incorrect sRGB profile
data = tf.keras.utils.image_dataset_from_directory(data_dir)
data_itr = data.as_numpy_iterator()
batch = data_itr.next()Found 781 files belonging to 3 classes.
2024-05-30 21:06:50.934017: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1
2024-05-30 21:06:50.934044: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB
2024-05-30 21:06:50.934049: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB
2024-05-30 21:06:50.934072: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2024-05-30 21:06:50.934090: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
2024-05-30 21:06:51.215918: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile
fig, ax = plt.subplots(ncols=4, figsize=(20,20))
for idx, img in enumerate(batch[0][:4]):
ax[idx].imshow(img.astype(int))
ax[idx].title.set_text(batch[1][idx])
# data = data.map(lambda x,y: (x/255, y))
# data_itr = data.as_numpy_iterator()
batch = data_itr.next()
batch[0][0].shape(256, 256, 3)
data_len = len(data)
train_len = int(data_len * 0.7)
val_len = int(data_len * 0.2)
test_len = int(data_len * 0.1) + 1
if train_len + val_len + test_len - data_len != 0:
print("Train: ", train_len, " Val: ", val_len, " Test: ", test_len, " Total: ", data_len, " Missing: ", train_len + val_len + test_len - data_len)
print("Please check padding")train_data = data.take(train_len)
val_data = data.skip(train_len).take(val_len)
test_data = data.skip(train_len+val_len).take(test_len)train_data<_TakeDataset element_spec=(TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(16, (3, 3), activation="relu", input_shape=(256, 256, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(32, (3, 3), activation="relu"),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(16, (3, 3), activation="relu"),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(256, activation="relu"),
tf.keras.layers.Dense(image_class_count),
])
model.compile(optimizer="adam", loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=["accuracy"])
model.summary()Model: "sequential_5"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ conv2d_15 (Conv2D) │ (None, 254, 254, 16) │ 448 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_15 (MaxPooling2D) │ (None, 127, 127, 16) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_16 (Conv2D) │ (None, 125, 125, 32) │ 4,640 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_16 (MaxPooling2D) │ (None, 62, 62, 32) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_17 (Conv2D) │ (None, 60, 60, 16) │ 4,624 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_17 (MaxPooling2D) │ (None, 30, 30, 16) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten_5 (Flatten) │ (None, 14400) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dropout_2 (Dropout) │ (None, 14400) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_10 (Dense) │ (None, 256) │ 3,686,656 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_11 (Dense) │ (None, 3) │ 771 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 3,697,139 (14.10 MB)
Trainable params: 3,697,139 (14.10 MB)
Non-trainable params: 0 (0.00 B)
logs_dir="logs"
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logs_dir)epochs = 20
hist = model.fit(train_data, validation_data=val_data, epochs=epochs, callbacks=[tensorboard_callback], verbose=0)
last_acc = hist.history["accuracy"][-1] * 100
print("Training Done, Accuracy: {0:.2f} %".format(last_acc))2024-05-30 21:24:32.880785: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile
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Training Done, Accuracy: 98.35 %
acc = hist.history
epoch_hist = hist.epoch
plt.figure()
plt.xlabel("EPOCH")
plt.ylabel("ACC")
plt.plot(acc["accuracy"])
plt.show()
test_loss, test_acc = model.evaluate(test_data, verbose=0)
print("Test Accuracy: {0:.2f}%".format(test_acc * 100))2024-05-30 21:32:16.900792: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile
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Test Accuracy: 96.10%
probability_model = tf.keras.Sequential([
model,
tf.keras.layers.Softmax(),
])
probability_model.summary()Model: "sequential_9"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ sequential_5 (Sequential) │ ? │ 3,697,139 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ softmax_3 (Softmax) │ ? │ 0 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 3,697,139 (14.10 MB)
Trainable params: 3,697,139 (14.10 MB)
Non-trainable params: 0 (0.00 B)
test_batch = test_data.as_numpy_iterator().next()2024-05-30 21:53:13.234825: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile
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X, y = test_batch
yhats = probability_model.predict(X)
fig, adx = plt.subplots(ncols= 4, figsize=(20,20))
classes = ['Cat', 'Chicken', 'Dog']
for idx, yhat in enumerate(yhats):
if idx > 3:
continue
img = X[idx]
prediction = np.argmax(yhat)
adx[idx].imshow(img.astype(int))
adx[idx].title.set_text(classes[prediction])�[1m1/1�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m0s�[0m 36ms/step
