FavioVazquez

from keras.applications.inception_v3 import preprocess_input
from keras.preprocessing.image import img_to_array, load_img
import numpy as np
from pyspark.sql.types import StringType
from sparkdl import KerasImageFileTransformer

def loadAndPreprocessKerasInceptionV3(uri):
  # this is a typical way to load and prep images in keras
    image = img_to_array(load_img(uri, target_size=(299, 299)))  # image dimensions for InceptionV3
    image = np.expand_dims(image, axis=0)

FavioVazquez

fs = !ls flower_photos/sample/*.jpg
uri_df = spark.createDataFrame(fs, StringType()).toDF("uri")
keras_pred_df = transformer.transform(uri_df)

FavioVazquez

from sparkdl import KerasTransformer
from keras.models import Sequential
from keras.layers import Dense
import numpy as np

# Generate random input data
num_features = 10
num_examples = 100
input_data = [{"features" : np.random.randn(num_features).astype(float).tolist()} for i in range(num_examples)]
schema = StructType([ StructField("features", ArrayType(FloatType()), True)])

FavioVazquez

from keras.applications import InceptionV3
from sparkdl.udf.keras_image_model import registerKerasImageUDF

registerKerasImageUDF("inceptionV3_udf", InceptionV3(weights="imagenet"))

FavioVazquez

from pyspark.ml.image import ImageSchema

image_df = ImageSchema.readImages("flower_photos/sample/")
image_df.registerTempTable("sample_images")

df = spark.sql("SELECT inceptionV3_udf_with_preprocessing(image) as predictions from sample_images").show(truncate=False)

FavioVazquez

from keras.applications import InceptionV3
from sparkdl.udf.keras_image_model import registerKerasImageUDF

def keras_load_img(fpath):
    from keras.preprocessing.image import load_img, img_to_array
    import numpy as np
    img = load_img(fpath, target_size=(299, 299))
    return img_to_array(img).astype(np.uint8)

registerKerasImageUDF("inceptionV3_udf_with_preprocessing", InceptionV3(weights="imagenet"), keras_load_img)

FavioVazquez

import numpy as np
import matplotlib.pyplot as plt

def softmax(z):
    z -= np.max(z)
    sm = (np.exp(z).T / np.sum(np.exp(z), axis=1))
    return sm


def initialize(dim1, dim2):

FavioVazquez

import numpy as np
import matplotlib.pyplot as plt

def softmax(z):
    z -= np.max(z)
    sm = (np.exp(z).T / np.sum(np.exp(z),axis=1))
    return sm

FavioVazquez

import numpy as np
import matplotlib.pyplot as plt


def softmax(z):
    cache = z
    z -= np.max(z)
    sm = (np.exp(z).T / np.sum(np.exp(z), axis=1))
    return sm, cache

FavioVazquez

'''README, Author - Anurag Kumar(mailto:[email protected])

Requirements:
  - sklearn
  - numpy
  - matplotlib

Python:
  - 3.5