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architecture_3.py
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architecture_3.py
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import numpy as np
import pandas as pd
from keras.models import Model
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, concatenate
from keras.preprocessing import image
import keras.backend as K
# train data generator
def train_data_generator(batch_size=100):
reader = pd.read_csv("train.txt", delimiter="#", low_memory=False, chunksize=batch_size, dtype=np.str)
while True:
try:
chunk = next(reader)
except StopIteration:
reader = pd.read_csv("train.txt", delimiter="#", low_memory=False, chunksize=batch_size, dtype=np.str)
chunk = next(reader)
chunk = chunk.to_numpy(dtype=np.str)
chunk_x_1 = []
chunk_x_2 = []
chunk_y = []
for row in chunk:
recipe_image = image.load_img("images/{}.jpg".format(row[7]))
recipe_image = image.img_to_array(recipe_image)
recipe_image = recipe_image.astype('float32') / 255
chunk_x_1.append([float(item) for item in row[0].split(',')])
chunk_x_2.append(recipe_image)
chunk_y.append(float(row[5]))
yield [np.array(chunk_x_1), np.array(chunk_x_2)], np.array(chunk_y)
# test data generator
def test_data_generator(batch_size=100):
reader = pd.read_csv("test.txt", delimiter="#", low_memory=False, chunksize=batch_size, dtype=np.str)
while True:
try:
chunk = next(reader)
except StopIteration:
reader = pd.read_csv("test.txt", delimiter="#", low_memory=False, chunksize=batch_size, dtype=np.str)
chunk = next(reader)
chunk = chunk.to_numpy(dtype=np.str)
chunk_x_1 = []
chunk_x_2 = []
chunk_y = []
for row in chunk:
recipe_image = image.load_img("images/{}.jpg".format(row[7]))
recipe_image = image.img_to_array(recipe_image)
recipe_image = recipe_image.astype('float32') / 255
chunk_x_1.append([float(item) for item in row[0].split(',')])
chunk_x_2.append(recipe_image)
chunk_y.append(float(row[5]))
yield [np.array(chunk_x_1), np.array(chunk_x_2)], np.array(chunk_y)
# rmse
def rmse(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))
# get data
batch_size = 32
user_input_dim = 4567
recipe_image_input_dim = (240, 320, 3)
num_total_samples = 2975564
num_training_samples = int(num_total_samples * 0.8)
num_testing_samples = num_total_samples - num_training_samples
train_data_generator_iterator = train_data_generator(batch_size)
test_data_generator_iterator = test_data_generator(batch_size)
test_data_generator_iterator2 = test_data_generator(batch_size)
# create model
# user
user_model_input = Input(shape=(user_input_dim,))
user_model = Dense(units=1024, activation='relu')(user_model_input)
user_model = Dense(units=512, activation='relu')(user_model)
user_model = Dense(units=256, activation='relu')(user_model)
user_model = Dense(units=128, activation='relu')(user_model)
# recipe image
recipe_image_model_input = Input(shape=(recipe_image_input_dim[0], recipe_image_input_dim[1], recipe_image_input_dim[2],))
recipe_image_model = Conv2D(32, (3, 3), activation='relu')(recipe_image_model_input)
recipe_image_model = MaxPooling2D((2, 2))(recipe_image_model)
recipe_image_model = Conv2D(64, (3, 3), activation='relu')(recipe_image_model)
recipe_image_model = MaxPooling2D((2, 2))(recipe_image_model)
recipe_image_model = Conv2D(128, (3, 3), activation='relu')(recipe_image_model)
recipe_image_model = MaxPooling2D((2, 2))(recipe_image_model)
recipe_image_model = Conv2D(256, (3, 3), activation='relu')(recipe_image_model)
recipe_image_model = MaxPooling2D((2, 2))(recipe_image_model)
recipe_image_model = Conv2D(256, (3, 3), activation='relu')(recipe_image_model)
recipe_image_model = MaxPooling2D((2, 2))(recipe_image_model)
recipe_image_model = Flatten()(recipe_image_model)
recipe_image_model = Dense(units=1024, activation='relu')(recipe_image_model)
recipe_image_model = Dense(units=512, activation='relu')(recipe_image_model)
recipe_image_model = Dense(units=256, activation='relu')(recipe_image_model)
recipe_image_model = Dense(units=128, activation='relu')(recipe_image_model)
# main model
main_model = concatenate([user_model, recipe_image_model])
main_model = Dense(units=256, activation='relu')(main_model)
main_model = Dense(units=128, activation='relu')(main_model)
main_model = Dense(units=64, activation='relu')(main_model)
main_model = Dense(units=1, activation='linear')(main_model)
model = Model(inputs=[user_model_input, recipe_image_model_input], outputs=main_model)
model.summary()
# compile
model.compile(
optimizer='adam',
loss=rmse,
metrics=['mse', 'mae', 'mape', rmse]
)
# fit
model.fit_generator(
generator=train_data_generator_iterator,
steps_per_epoch=(num_training_samples // batch_size) + 1,
epochs=3,
verbose=1,
)
# save
model.save_weights('architecture_3.h5')
# evaluate
metrics = model.evaluate_generator(
generator=test_data_generator_iterator,
steps=(num_testing_samples // batch_size)+1,
verbose=1,
)
print('metrics:', metrics)
# predict
prediction = model.predict_generator(
generator=test_data_generator_iterator2,
steps=(num_testing_samples // batch_size) + 1,
verbose=1,
)
print('prediction:', prediction)
pd.DataFrame(prediction).to_csv("architecture_3.csv", index=False)