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{"class_name": "Sequential", "keras_version": "1.1.1", "config": [{"class_name": "Convolution2D", "config": {"b_regularizer": null, "W_constraint": null, "b_constraint": null, "name": "convolution2d_1", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 3, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 32, "input_dtype": "float32", "border_mode": "same", "batch_input_shape": [null, 3, 32, 32], "W_regularizer": null, "activation": "linear", "nb_row": 3}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_1"}}, {"class_name": "Convolution2D", "config": {"W_constraint": null, "b_constraint": null, "name": "convolution2d_2", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 3, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 32, "border_mode": "same", "b_regularizer": null, "W_regularizer": null, "activation": "linear", "nb_row": 3}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_2"}}, {"class_name": "MaxPooling2D", "config": {"name": "maxpooling2d_1", "trainable": true, "dim_ordering": "th", "pool_size": [2, 2], "strides": [2, 2], "border_mode": "valid"}}, {"class_name": "Dropout", "config": {"p": 0.25, "trainable": true, "name": "dropout_1"}}, {"class_name": "Convolution2D", "config": {"W_constraint": null, "b_constraint": null, "name": "convolution2d_3", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 3, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 64, "border_mode": "same", "b_regularizer": null, "W_regularizer": null, "activation": "linear", "nb_row": 3}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_3"}}, {"class_name": "Convolution2D", "config": {"W_constraint": null, "b_constraint": null, "name": "convolution2d_4", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 3, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 64, "border_mode": "valid", "b_regularizer": null, "W_regularizer": null, "activation": "linear", "nb_row": 3}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_4"}}, {"class_name": "MaxPooling2D", "config": {"name": "maxpooling2d_2", "trainable": true, "dim_ordering": "th", "pool_size": [2, 2], "strides": [2, 2], "border_mode": "valid"}}, {"class_name": "Dropout", "config": {"p": 0.25, "trainable": true, "name": "dropout_2"}}, {"class_name": "Flatten", "config": {"trainable": true, "name": "flatten_1"}}, {"class_name": "Dense", "config": {"W_constraint": null, "b_constraint": null, "name": "dense_1", "activity_regularizer": null, "trainable": true, "init": "glorot_uniform", "bias": true, "input_dim": null, "b_regularizer": null, "W_regularizer": null, "activation": "linear", "output_dim": 512}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_5"}}, {"class_name": "Dropout", "config": {"p": 0.5, "trainable": true, "name": "dropout_3"}}, {"class_name": "Dense", "config": {"W_constraint": null, "b_constraint": null, "name": "dense_2", "activity_regularizer": null, "trainable": true, "init": "glorot_uniform", "bias": true, "input_dim": null, "b_regularizer": null, "W_regularizer": null, "activation": "linear", "output_dim": 10}}, {"class_name": "Activation", "config": {"activation": "softmax", "trainable": true, "name": "activation_6"}}]} |
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# The script MUST contain a function named azureml_main | ||
# which is the entry point for this module. | ||
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# imports up here can be used to | ||
import pandas as pd | ||
import theano | ||
import theano.tensor as T | ||
from theano import function | ||
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from keras.models import Sequential | ||
from keras.layers import Dense, Activation | ||
import numpy as np | ||
# The entry point function can contain up to two input arguments: | ||
# Param<dataframe1>: a pandas.DataFrame | ||
# Param<dataframe2>: a pandas.DataFrame | ||
def azureml_main(dataframe1 = None, dataframe2 = None): | ||
# Execution logic goes here | ||
# print('Input pandas.DataFrame #1:\r\n\r\n{0}'.format(dataframe1)) | ||
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# If a zip file is connected to the third input port is connected, | ||
# it is unzipped under ".\Script Bundle". This directory is added | ||
# to sys.path. Therefore, if your zip file contains a Python file | ||
# mymodule.py you can import it using: | ||
# import mymodule | ||
model = Sequential() | ||
model.add(Dense(1, input_dim=784, activation="relu")) | ||
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) | ||
data = np.random.random((1000,784)) | ||
labels = np.random.randint(2, size=(1000,1)) | ||
model.fit(data, labels, nb_epoch=10, batch_size=32) | ||
model.evaluate(data, labels) | ||
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return dataframe1, |
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from keras.applications.vgg16 import VGG16 | ||
from keras.models import Model | ||
from keras.preprocessing import image | ||
from keras.applications.vgg16 import preprocess_input | ||
import numpy as np | ||
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# pre-built and pre-trained deep learning VGG16 model | ||
base_model = VGG16(weights='imagenet', include_top=True) | ||
for i, layer in enumerate(base_model.layers): | ||
print (i, layer.name, layer.output_shape) | ||
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# extract features from block4_pool block | ||
model = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output) | ||
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img_path = 'cat.jpg' | ||
img = image.load_img(img_path, target_size=(224, 224)) | ||
x = image.img_to_array(img) | ||
x = np.expand_dims(x, axis=0) | ||
x = preprocess_input(x) | ||
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# get the features from this block | ||
features = model.predict(x) | ||
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print features |