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main.py
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main.py
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# %%
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
base_dir = 'data'
datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=10,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.1,
zoom_range=0.1,
validation_split=0.2)
train_generator = datagen.flow_from_directory(
base_dir,
target_size=(28, 28),
color_mode='grayscale',
subset='training'
)
validation_generator = datagen.flow_from_directory(
base_dir,
target_size=(28, 28),
color_mode='grayscale',
batch_size=250,
subset='validation'
)
# %%
import tensorflow as tf
from tensorflow.python.keras.models import Model
from tensorflow.python.keras import layers
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.optimizers import RMSprop, Adam
from tensorflow.python.keras.callbacks import TensorBoard
K.clear_session()
img_input = layers.Input(shape=(28, 28, 1))
x = layers.Conv2D(32, 3, activation='relu')(img_input)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.MaxPooling2D(2, 2)(x)
x = layers.Dropout(0.25)(x)
x = layers.Flatten()(x)
x = layers.Dense(128, activation='relu')(x)
x = layers.Dropout(0.5)(x)
output = layers.Dense(26, activation='softmax')(x)
model = Model(img_input, output)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(),
metrics=['acc'])
# %%
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=10,
validation_data=validation_generator,
validation_steps=50,
verbose=2)
# %%
model.save('letter-model.h5')