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import pandas as pd | |
df_train = pd.read_csv('./KagglePlanetMCML.csv') | |
df_train.head() |
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import tensorflow as tf | |
IM_SIZE = 128 | |
image_input = tf.keras.Input(shape=(IM_SIZE, IM_SIZE, 3), name='input_layer') | |
# Some convolutional layers | |
conv_1 = tf.keras.layers.Conv2D(32, | |
kernel_size=(3, 3), | |
padding='same', | |
activation='relu')(image_input) |
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print(model.summary()) |
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model.compile(optimizer='adam', | |
loss={'weather': 'categorical_crossentropy', | |
'ground': 'binary_crossentropy'}) |
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import ast | |
import numpy as np | |
import math | |
import os | |
import random | |
from tensorflow.keras.preprocessing.image import img_to_array as img_to_array | |
from tensorflow.keras.preprocessing.image import load_img as load_img | |
def load_image(image_path, size): | |
# data augmentation logic such as random rotations can be added here |
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callbacks = [ | |
tf.keras.callbacks.ModelCheckpoint('./model.h5', verbose=1) | |
] | |
model.fit_generator(generator=seq, | |
verbose=1, | |
epochs=1, | |
use_multiprocessing=True, | |
workers=4, | |
callbacks=callbacks) |
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seq = KagglePlanetSequence('./KagglePlanetMCML.csv', | |
'./data/train/', | |
im_size=IM_SIZE, | |
batch_size=32) |
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another_model = tf.keras.models.load_model('./model.h5') | |
another_model.fit_generator(generator=seq, verbose=1, epochs=1) |
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import tensorflow as tf | |
from tensorflow.python.saved_model import builder as saved_model_builder | |
from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def | |
from tensorflow.python.saved_model import tag_constants | |
tf.keras.backend.set_learning_phase(0) | |
# The export path contains the name and the version of the model | |
export_path = './PlanetModel/1' | |
model = tf.keras.models.load_model('./model.h5') | |
builder = saved_model_builder.SavedModelBuilder(export_path) |
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import requests | |
import json | |
image = img_to_array(load_img('./data/train/train_10001.jpg', target_size=(128,128))) / 255. | |
payload = { | |
"instances": [{'input_image': image.tolist()}] | |
} | |
r = requests.post('http://localhost:9000/v1/models/PlanetModel:predict', json=payload) | |
json.loads(r.content) |
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