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train.py
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train.py
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from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten, MaxPool2D
from keras.layers.convolutional import Conv2D
from sklearn.model_selection import train_test_split
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, MaxPooling2D, Dropout, Conv2D
from tensorflow.keras import optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255, width_shift_range=0.05, height_shift_range=0.05)
train_generator = train_datagen.flow_from_directory(
'data/train',
target_size=(28,28),
batch_size=1,
class_mode='categorical')
validation_generator = train_datagen.flow_from_directory(
'data/val',
target_size=(28,28),
batch_size=1,
class_mode='categorical')
model = Sequential()
model.add(Conv2D(32, (24,24), input_shape=(28, 28, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(36, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(lr=0.00001), metrics=['accuracy'])
batch_size = 1
model.fit_generator(
train_generator,
steps_per_epoch = train_generator.samples,
validation_data = validation_generator,
validation_steps = validation_generator.samples,
epochs = 80)
model.save("model_retrain.h5")