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# Create an interactive Tensorflow session
sess = tf.InteractiveSession()
# These will be inputs for the model
# Input pixels of images, flattened
# 1296 = 36*36 which is the size of images
x = tf.placeholder("float", [None, 1296])
## Known labels
y_ = tf.placeholder("float", [None,2])
# Computes softmax cross entropy between logits and labels
# Measures the probability error in discrete classification tasks
# For example, each font image is labeled with one and only one label: an image can be font SansSerif or Serif, but not both.
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(
logits = y + 1e-50, labels = y_))
# GradientDescentOptimizer is used to minimize loss
train_step = tf.train.GradientDescentOptimizer(
0.02).minimize(cross_entropy)
# Create an interactive Tensorflow session
sess = tf.InteractiveSession()
# These will be inputs for the model
# Input pixels of images, flattened
# 1296 = 36*36 which is the size of images
x = tf.placeholder("float", [None, 1296])
# Known labels
y_ = tf.placeholder("float", [None,2])
from itertools import chain, starmap
def flatten_json_iterative_solution(dictionary):
"""Flatten a nested json file"""
def unpack(parent_key, parent_value):
"""Unpack one level of nesting in json file"""
# Unpack one level only!!!
if isinstance(parent_value, dict):
def flatten_json(nested_json):
"""
Flatten json object with nested keys into a single level.
Args:
nested_json: A nested json object.
Returns:
The flattened json object if successful, None otherwise.
"""
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@alinazhanguwo
alinazhanguwo / flatten_json_recursively.ipynb
Created November 14, 2018 19:35
flatten_json_recursively.ipynb
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@alinazhanguwo
alinazhanguwo / flattenjsonrecursive.ipynb
Last active November 14, 2018 19:33
Traditional recursive python solution for flattening JSON
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