DeepLearning Tensorflow Tuts

Adding a simple to understand deep learning with Tensorflow and
examining stuff with Tensorboard

Author: TarrySingh

deep-learning/tensor-flow-exercises/deepnet.py

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+# Importing TF, Mnist
+from __future__ import print_function
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets('tmp/data/', one_hot=True)
+
+# Define hidden layers
+n_nodes_hlayer1 = 500
+n_nodes_hlayer2 = 500
+n_nodes_hlayer3 = 500
+
+# define how many runs and how many pics to run to do backprop, manipulate wgts and repeat & no of epochs you want to run
+n_classes = 10
+batch_size = 100
+hm_epoch = 10
+logs_path = '/tmp/tensorflow_logs/example'
+
+# Height & Width / Input features of size = 28x28 pix = 784
+
+x = tf.placeholder('float', [None, 784])
+y = tf.placeholder('float')
+
+def nn_model(data):
+    # simply put this model = inputs data*weights + biases
+    hidden_layer_1 = {'weights':tf.Variable(tf.random_normal([784, n_nodes_hlayer1])),
+        'biases':tf.Variable(tf.random_normal([n_nodes_hlayer1]))}
+
+    hidden_layer_2 = {'weights':tf.Variable(tf.random_normal([n_nodes_hlayer1, n_nodes_hlayer2])),
+        'biases':tf.Variable(tf.random_normal([n_nodes_hlayer2]))}
+
+hidden_layer_3 = {'weights':tf.Variable(tf.random_normal([n_nodes_hlayer2, n_nodes_hlayer3])),
+    'biases':tf.Variable(tf.random_normal([n_nodes_hlayer3]))}
+    
+    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hlayer3, n_classes])),
+        'biases':tf.Variable(tf.random_normal([n_classes]))}
+
+    layer1 = tf.add(tf.matmul(data, hidden_layer_1['weights']), hidden_layer_1['biases'])
+    layer1 = tf.nn.relu(layer1)
+
+layer2 = tf.add(tf.matmul(layer1, hidden_layer_2['weights']), hidden_layer_2['biases'])
+    layer2 = tf.nn.relu(layer2)
+    
+    layer3 = tf.add(tf.matmul(layer2, hidden_layer_3['weights']), hidden_layer_3['biases'])
+    layer3 = tf.nn.relu(layer3)
+    
+    output = tf.add(tf.matmul(layer3, output_layer['weights']), output_layer['biases'])
+    
+    return output
+
+# We define function to train this NN
+def train_nn(x):
+    prediction = nn_model(x)
+    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
+    # Watch out to pick Adam which is same as SGD
+    optimizer = tf.train.AdamOptimizer().minimize(cost) #WTF Adadelta!!!!
+    
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer()) #This changed in TF 1.0 version onwards
+        # training it
+        for epoch in range (hm_epoch):
+            epoch_loss = 0
+            for _ in range ( int ( mnist.train.num_examples / batch_size)):
+                epoch_x , epoch_y = mnist.train.next_batch (batch_size)
+                _ , c = sess.run([optimizer, cost], feed_dict={x:epoch_x, y:epoch_y})
+                #code that will optimize the weights and biases
+                epoch_loss += c
+            print('Epochs', epoch, 'completed out of', hm_epoch, 'loss:', epoch_loss)
+        
+        #Testing
+        correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
+        accuracy = tf.reduce_mean(tf.cast (correct, 'float'))
+        print('Accuracy:', accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))
+
+train_nn(x)
+
+# Examine results with Tensorboard
+
+print("Run the command line:\n" \
+      "--> tensorboard --logdir=/tmp/tensorflow_logs " \
+      "\nThen open http://0.0.0.0:6006/ into your web browser")