alinazhanguwo

alinazhanguwo

alinazhanguwo

alinazhanguwo

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.
    """

alinazhanguwo

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):

alinazhanguwo

# 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])

alinazhanguwo

# 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)

alinazhanguwo

# 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])

alinazhanguwo

# 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(0.01).minimize(cross_entropy)

# Define accuracy

alinazhanguwo

# 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])