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From Ulyana Merzlyakova, 9 Months ago, written in Plain Text, viewed 114 times.
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  1. from tensorflow.keras.datasets import fashion_mnist
  2. from tensorflow.keras.optimizers import Adam
  3. import numpy as np
  4. from tensorflow.keras.layers import Conv2D, Flatten, Dense, AvgPool2D, GlobalAveragePooling2D
  5. from tensorflow import keras
  6. from tensorflow.keras import Sequential
  7. from tensorflow.keras.applications.resnet import ResNet50
  8. import matplotlib.pyplot as plt
  9. from tensorflow.keras.preprocessing.image import ImageDataGenerator
  10.  
  11. def load_train(path):
  12.     labels = pd.read_csv(path+'labels.csv')
  13.     train_datagen = ImageDataGenerator(rescale=1/255,
  14.                                        validation_split=0.25)
  15.     train_gen_flow = train_datagen.flow_from_dataframe(
  16.         dataframe=labels,
  17.         directory=path+'final_files/',
  18.         x_col='file_name',
  19.         y_col='real_age',
  20.         target_size=(224, 224),
  21.         batch_size=32,
  22.         class_mode='raw',
  23.         subset='training',
  24.         seed=12345)
  25.     return train_gen_flow
  26.  
  27. def load_test(path):
  28.     labels = pd.read_csv(path+'labels.csv')
  29.     test_datagen = ImageDataGenerator(rescale=1/255,
  30.                                        validation_split=0.25)
  31.     test_gen_flow = test_datagen.flow_from_dataframe(
  32.         dataframe=labels,
  33.         directory=path+'final_files/',
  34.         x_col='file_name',
  35.         y_col='real_age',
  36.         target_size=(224, 224),
  37.         batch_size=32,
  38.         class_mode='raw',
  39.         subset='validation',
  40.         seed=12345)
  41.     return test_gen_flow
  42.  
  43. def create_model(input_shape):
  44.    
  45.     model = Sequential()
  46.     model.add(Conv2D(filters=6, kernel_size=(5, 5), padding='same',
  47.                  activation="relu", input_shape=input_shape))
  48.     model.add(Conv2D(filters=16, kernel_size=(5, 5),
  49.                  activation="relu"))
  50.     model.add(Flatten())
  51.     model.add(Dense(1, activation='relu'))
  52.     opt = Adam(lr=0.0007)
  53.     model.compile(loss='mean_squared_error', optimizer=opt, metrics=['mae'])
  54.     return model
  55.  
  56.  
  57. def train_model(model, train_data, test_data, batch_size=None, epochs=2,
  58.                 steps_per_epoch=None, validation_steps=None):
  59.  
  60.  
  61.     model.fit(train_data, validation_data=test_data, verbose=2, epochs=epochs,
  62.               batch_size=batch_size,
  63.               steps_per_epoch=steps_per_epoch,
  64.               validation_steps=validation_steps)
  65.  
  66.     return model

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