tensorflow-解决3个问题

问题一：对特征归一化

Min-Max Scaling:
X′=a+(X−Xmin)(b−a)/(Xmax−Xmin)

# Problem 1 - Implement Min-Max scaling for grayscale image data
def normalize_grayscale(image_data):
    """
    Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]
    :param image_data: The image data to be normalized
    :return: Normalized image data
    """
    # TODO: Implement Min-Max scaling for grayscale image data
    a = 0.1
    b = 0.9
    grayscal_min=0
    grayscal_max=255
    return a + (((image_data-grayscal_min)*(b-a))/(grayscal_max-grayscal_min))

问题二：用 TensorFlow 创建特征、目标、权重和偏置项 tensor。

# All the pixels in the image (28 * 28 = 784)
features_count = 784
# All the labels
labels_count = 10

# TODO: Set the features and labels tensors
features = tf.placeholder(tf.float32)
labels = tf.placeholder(tf.float32)

# TODO: Set the weights and biases tensors
weights = tf.Variable(tf.truncated_normal((features_count,labels_count)))

biases = tf.Variable(tf.zeros(labels_count))

### DON'T MODIFY ANYTHING BELOW ###

#Test Cases
from tensorflow.python.ops.variables import Variable

assert features._op.name.startswith('Placeholder'), 'features must be a placeholder'
assert labels._op.name.startswith('Placeholder'), 'labels must be a placeholder'
assert isinstance(weights, Variable), 'weights must be a TensorFlow variable'
assert isinstance(biases, Variable), 'biases must be a TensorFlow variable'

assert features._shape == None or (\
    features._shape.dims[0].value is None and\
    features._shape.dims[1].value in [None, 784]), 'The shape of features is incorrect'
assert labels._shape  == None or (\
    labels._shape.dims[0].value is None and\
    labels._shape.dims[1].value in [None, 10]), 'The shape of labels is incorrect'
assert weights._variable._shape == (784, 10), 'The shape of weights is incorrect'
assert biases._variable._shape == (10), 'The shape of biases is incorrect'

assert features._dtype == tf.float32, 'features must be type float32'
assert labels._dtype == tf.float32, 'labels must be type float32'

# Feed dicts for training, validation, and test session
train_feed_dict = {features: train_features, labels: train_labels}
valid_feed_dict = {features: valid_features, labels: valid_labels}
test_feed_dict = {features: test_features, labels: test_labels}

# Linear Function WX + b
logits = tf.matmul(features, weights) + biases

prediction = tf.nn.softmax(logits)

# Cross entropy
cross_entropy = -tf.reduce_sum(labels * tf.log(prediction), reduction_indices=1)

# Training loss
loss = tf.reduce_mean(cross_entropy)

# Create an operation that initializes all variables
init = tf.global_variables_initializer()

# Test Cases
with tf.Session() as session:
    session.run(init)
    session.run(loss, feed_dict=train_feed_dict)
    session.run(loss, feed_dict=valid_feed_dict)
    session.run(loss, feed_dict=test_feed_dict)
    biases_data = session.run(biases)

assert not np.count_nonzero(biases_data), 'biases must be zeros'

print('Tests Passed!')

问题三：调整学习率，epochs 和 batch size 来获取最高准确率