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import tensorflow as tf
import os
import pathlib
import numpy as np
data_dir = pathlib.Path("/Users/rosh/Downloads/Train_data")
class_names = np.array(sorted([item.name for item in data_dir.glob("*")]))
class_names = list(class_names)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import random
def view_random_image(target_dir, target_class):
target_folder = target_dir + "/" + target_class
random_image = random.sample(os.listdir(target_folder), 1)
img = mpimg.imread(target_folder + "/" + random_image[0])
plt.imshow(img)
plt.title(target_class)
plt.axis("off")
print(f"Image shape: {img.shape}")
plt.show()
return img
#img = view_random_image(target_dir="/Users/rosh/Downloads/Train_data",target_class)
from tensorflow.keras.preprocessing.image import ImageDataGenerator
tf.random.set_seed(42)
# Define data augmentation parameters
train_datagen = ImageDataGenerator(
rotation_range=20, # Random rotation in the range [-20, 20] degrees
width_shift_range=0.1, # Random horizontal shift by up to 10% of the width
height_shift_range=0.1, # Random vertical shift by up to 10% of the height
shear_range=0.2, # Shear intensity (shear angle in radians)
zoom_range=0.2, # Random zoom in the range [0.8, 1.2]
horizontal_flip=True, # Random horizontal flipping
vertical_flip=True, # Random vertical flipping
fill_mode='nearest', # Fill mode for points outside the input boundaries
rescale=1./255 # Rescaling factor
)
valid_datagen = ImageDataGenerator(
rescale=1./255 # Rescaling factor
)
train_dir = "/Users/rosh/Downloads/Train_data"
valid_dir = "/Users/rosh/Downloads/Validation_data"
train_data = train_datagen.flow_from_directory(directory=train_dir,
batch_size=32,
target_size=(224, 224),
class_mode="categorical",
seed=42)
valid_data = valid_datagen.flow_from_directory(directory=valid_dir,
batch_size=32,
target_size=(224, 224),
class_mode="categorical",
seed=42)
model_1 = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation="relu", input_shape=(224, 224, 3)),
tf.keras.layers.MaxPool2D(pool_size=2, padding="valid"),
tf.keras.layers.Conv2D(64, 3, activation="relu"),
tf.keras.layers.MaxPool2D(2),
tf.keras.layers.Conv2D(64, 3, activation="relu"),
tf.keras.layers.MaxPool2D(2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dense(10, activation="softmax")
])
model_1.compile(loss=tf.keras.losses.CategoricalCrossentropy(),
optimizer=tf.keras.optimizers.Adam(),
metrics=["accuracy"])
history = model_1.fit(train_data,
epochs=100,
steps_per_epoch=len(train_data),
validation_data=valid_data,
validation_steps=len(valid_data),
verbose=1)
model_1.save("model_3.h5")
#
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