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mnist_2.0_five_layers_sigmoid.py
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mnist_2.0_five_layers_sigmoid.py
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# encoding: UTF-8
# Copyright 2016 Google.com
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf
import originals
from tensorflow.examples.tutorials.mnist import input_data as mnist_data
print("Tensorflow version " + tf.__version__)
tf.set_random_seed(0)
# neural network with 1 layer of 10 softmax neurons
#
# · · · · · · · · · · (input data, flattened pixels) X [batch, 784] # 784 = 28 * 28
# \x/x\x/x\x/x\x/x\x/ -- fully connected layer (softmax) W [784, 10] b[10]
# · · · · · · · · Y [batch, 10]
# The model is:
#
# Y = softmax( X * W + b)
# X: matrix for 100 grayscale images of 28x28 pixels, flattened (there are 100 images in a mini-batch)
# W: weight matrix with 784 lines and 10 columns
# b: bias vector with 10 dimensions
# +: add with broadcasting: adds the vector to each line of the matrix (numpy)
# softmax(matrix) applies softmax on each line
# softmax(line) applies an exp to each value then divides by the norm of the resulting line
# Y: output matrix with 100 lines and 10 columns
# Download images and labels into mnist.test (10K images+labels) and mnist.train (60K images+labels)
mnist = mnist_data.read_data_sets("data", one_hot=True, reshape=False, validation_size=0)
# input X: 28x28 grayscale images, the first dimension (None) will index the images in the mini-batch
X = tf.placeholder(tf.float32, [None, 28, 28, 1])
# correct answers will go here
Y_ = tf.placeholder(tf.float32, [None, 10])
X_flat = tf.reshape(X, shape=[-1, 784])
#W1 = tf.get_variable('W1', [784, 200], initializer=tf.random_normal_initializer(stddev=0.1))
W1 = tf.Variable(tf.truncated_normal([784, 200], stddev=0.1))
#b1 = tf.get_variable('b1', [200], initializer=tf.zeros_initializer)
b1 = tf.Variable(tf.ones([200])/10)
#W2 = tf.get_variable('W2', [200, 100], initializer=tf.random_normal_initializer(stddev=0.1))
W2 = tf.Variable(tf.truncated_normal([200, 100], stddev=0.1))
#b2 = tf.get_variable('b2', [100], initializer=tf.zeros_initializer)
b2 = tf.Variable(tf.ones([100])/10)
#W3 = tf.get_variable('W3', [100, 60], initializer=tf.random_normal_initializer(stddev=0.1))
W3 = tf.Variable(tf.truncated_normal([100, 60], stddev=0.1))
#b3 = tf.get_variable('b3', [60], initializer=tf.zeros_initializer)
b3 = tf.Variable(tf.ones([60])/10)
#W4 = tf.get_variable('W4', [60, 30], initializer=tf.random_normal_initializer(stddev=0.1))
W4 = tf.Variable(tf.truncated_normal([60, 30], stddev=0.1))
#b4 = tf.get_variable('b4', [30], initializer=tf.zeros_initializer)
b4 = tf.Variable(tf.ones([30])/10)
#W5 = tf.get_variable('W5', [30, 10], initializer=tf.random_normal_initializer(stddev=0.1))
W5 = tf.Variable(tf.truncated_normal([30, 10], stddev=0.1))
#b5 = tf.get_variable('b5', [10], initializer=tf.zeros_initializer)
b5 = tf.Variable(tf.ones([10])/10)
a1 = tf.nn.relu(tf.matmul(X_flat, W1) + b1)
a2 = tf.nn.relu(tf.matmul(a1, W2) + b2)
a3 = tf.nn.relu(tf.matmul(a2, W3) + b3)
a4 = tf.nn.relu(tf.matmul(a3, W4) + b4)
Ylogits = tf.matmul(a4, W5) + b5
Y = tf.nn.softmax(Ylogits)
# flatten the images into a single line of pixels
# -1 in the shape definition means "the only possible dimension that will preserve the number of elements"
#XX = tf.reshape(X, [-1, 784])
# The model
#Y = tf.nn.softmax(tf.matmul(XX, W) + b)
# loss function: cross-entropy = - sum( Y_i * log(Yi) )
# Y: the computed output vector
# Y_: the desired output vector
# cross-entropy
# log takes the log of each element, * multiplies the tensors element by element
# reduce_mean will add all the components in the tensor
# so here we end up with the total cross-entropy for all images in the batch
# cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y)) * 1000.0 # normalized for batches of 100 images,
# # *10 because "mean" included an unwanted division by 10
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)) * 100
# accuracy of the trained model, between 0 (worst) and 1 (best)
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# training, learning rate = 0.005
train_step = tf.train.AdamOptimizer(0.003).minimize(cross_entropy)
# matplotlib visualisation
allweights = tf.concat([tf.reshape(W1, [-1]),
tf.reshape(W2, [-1]),
tf.reshape(W3, [-1]),
tf.reshape(W4, [-1]),
tf.reshape(W5, [-1])], 0)
allbiases = tf.concat([tf.reshape(b1, [-1]),
tf.reshape(b2, [-1]),
tf.reshape(b3, [-1]),
tf.reshape(b4, [-1]),
tf.reshape(b5, [-1])], 0)
I = originals.tensorflowvisu.tf_format_mnist_images(X, Y, Y_) # assembles 10x10 images by default
It = originals.tensorflowvisu.tf_format_mnist_images(X, Y, Y_, 1000, lines=25) # 1000 images on 25 lines
datavis = originals.tensorflowvisu.MnistDataVis()
# init
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
# You can call this function in a loop to train the model, 100 images at a time
def training_step(i, update_test_data, update_train_data):
# training on batches of 100 images with 100 labels
batch_X, batch_Y = mnist.train.next_batch(100)
# compute training values for visualisation
if update_train_data:
a, c, im, w, b = sess.run([accuracy, cross_entropy, I, allweights, allbiases], feed_dict={X: batch_X, Y_: batch_Y})
datavis.append_training_curves_data(i, a, c)
datavis.append_data_histograms(i, w, b)
datavis.update_image1(im)
print(str(i) + ": accuracy:" + str(a) + " loss: " + str(c))
# compute test values for visualisation
if update_test_data:
a, c, im = sess.run([accuracy, cross_entropy, It], feed_dict={X: mnist.test.images, Y_: mnist.test.labels})
datavis.append_test_curves_data(i, a, c)
datavis.update_image2(im)
print(str(i) + ": ********* epoch " + str(i*100//mnist.train.images.shape[0]+1) + " ********* test accuracy:" + str(a) + " test loss: " + str(c))
# the backpropagation training step
sess.run(train_step, feed_dict={X: batch_X, Y_: batch_Y})
datavis.animate(training_step, iterations=5000+1, train_data_update_freq=10, test_data_update_freq=50, more_tests_at_start=True)
# to save the animation as a movie, add save_movie=True as an argument to datavis.animate
# to disable the visualisation use the following line instead of the datavis.animate line
# for i in range(2000+1): training_step(i, i % 50 == 0, i % 10 == 0)
print("max test accuracy: " + str(datavis.get_max_test_accuracy()))
# final max test accuracy = 0.9268 (10K iterations). Accuracy should peak above 0.92 in the first 2000 iterations.