# Number of features (28*28 image is 784 features)
n_features = 784
# Number of labels
n_labels = 3
# Features and Labels
features = tf.placeholder(tf.float32)
labels = tf.placeholder(tf.float32)
# Weights and Biases
w = get_weights(n_features, n_labels)
b = get_biases(n_labels)
# Linear Function xW + b
logits = linear(features, w, b)
# Training data
train_features, train_labels = mnist_features_labels(n_labels)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
# Softmax functiond to normalize the varables from 0 to 1
# variables with heavier weights weigh more than the ones with lighter weight
prediction = tf.nn.softmax(logits)
# Cross entropy
# This quantifies how far off the predictions were.
cross_entropy = -tf.reduce_sum(labels * tf.log(prediction),
reduction_indices=1)
# Number of features (28*28 image is 784 features)
n_features = 784
# Number of labels
n_labels = 3
# Features and Labels
features = tf.placeholder(tf.float32)
labels = tf.placeholder(tf.float32)
# Weights and Biases
w = weights(n_features, n_labels)
b = biases(n_labels)
# Linear Function xW + b
logits = linear(features, w, b)
# Training data
train_features, train_labels = mnist_features_labels(n_labels)
with tf.Session() as session:
# TODO: Initialize session variables
session.run(tf.initialize_variables(w))
# Softmax
prediction = tf.nn.softmax(logits)
# Cross entropy
# This quantifies how far off the predictions were.
# You'll learn more about this in future lessons.
cross_entropy = -tf.reduce_sum(labels * tf.log(prediction), reduction_indices=1)
# Number of features (28 * 28 image is 784 features)
n_features = 784
# Number of labels
n_labels = 3
# Features and Labels
features = tf.placeholder(tf.float32)
labels = tf.placeholder(tf.float32)
# Weights and biases
weights = get_weights(n_features, n_labels)
biases = get_biases(n_labels)
# Linear Function
logits = linear(features, weights, biases)
# Training Data
train_features, train_labels = mnist_features_labels(n_labels)
with tf.Session() as sess:
# Initialize session variables
sess.run(tf.global_variables_initializer())
# Softmax
prediction = tf.nn.softmax(
logits) # create probabilities from logit scores
# Cross entropy
# This quantfies how far off the predictions were
cross_entropy = -tf.reduce_sum(labels * tf.log(prediction),
# Number of features (28*28 image is 784 features)
n_features = 784
# Number of labels
n_labels = 3
# Features and Labels
features = tf.placeholder(tf.float32)
labels = tf.placeholder(tf.float32)
# Weights and Biases
W = get_weights(n_features,n_labels)
b = get_biases(n_labels)
# Linear Function xW + b
logits = linear(features,W,b)
# Training data
train_features,train_labels = mnist_features_labels(n_labels)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
prediction = tf.nn.softmax(logits)
# Cross entropy
# This quantifies how far off the prediction were.
cross_entropy = -tf.reduce_sum(labels * tf.log(prediction),reduction_indices=1)
# Training loss
loss = tf.reduce_mean(cross_entropy)
