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- Book:TensorFlow Tutorials (nlintz)
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import tensorflow as tfa = tf.placeholder( "float" ) # Create a symbolic variable 'a' b = tf.placeholder( "float" ) # Create a symbolic variable 'b' y = tf.multiply(a, b) # multiply the symbolic variableswith tf.Session() as sess: # create a session to evaluate the symbolic expressions print( "%f should equal 2.0" % sess.run(y, feed_dict={a: , b: })) # eval expressions with parameters for a and b print( "%f should equal 9.0" % sess.run(y, feed_dict={a: , b: }))2.000000 should equal 2.09.000000 should equal 9.0import tensorflow as tf import numpy as nptrX = np.linspace( -1 , , )trY = * trX + np.random.randn(*trX.shape) * 0.33 # create a y value which is approximately linear but with some random noiseX = tf.placeholder( "float" ) # create symbolic variables Y = tf.placeholder( "float" ) def model (X, w) : return tf.multiply(X, w) # lr is just X*w so this model line is pretty simple w = tf.Variable( 0.0 , name= "weights" ) # create a shared variable (like theano.shared) for the weight matrix y_model = model(X, w)cost = tf.square(Y - y_model) # use square error for cost function train_op = tf.train.GradientDescentOptimizer( 0.01 ).minimize(cost) # construct an optimizer to minimize cost and fit line to my data# Launch the graph in a session with tf.Session() as sess: # you need to initialize variables (in this case just variable W) tf.global_variables_initializer().run() for i in range(): for (x, y) in zip(trX, trY): sess.run(train_op, feed_dict={X: x, Y: y}) print(sess.run(w)) # It should be something around 22.00863import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_datadef init_weights (shape) : return tf.Variable(tf.random_normal(shape, stddev= 0.01 )) def model (X, w) : return tf.matmul(X, w) # notice we use the same model as linear regression, this is because there is a baked in cost function which performs softmax and cross entropy mnist = input_data.read_data_sets( "MNIST_data/" , one_hot= True )trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labelsX = tf.placeholder( "float" , [ None , ]) # create symbolic variables Y = tf.placeholder( "float" , [ None , ])w = init_weights([, ]) # like in linear regression, we need a shared variable weight matrix for logistic regression py_x = model(X, w)cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y)) # compute mean cross entropy (softmax is applied internally) train_op = tf.train.GradientDescentOptimizer( 0.05 ).minimize(cost) # construct optimizer predict_op = tf.argmax(py_x, ) # at predict time, evaluate the argmax of the logistic regression# Launch the graph in a session with tf.Session() as sess: # you need to initialize all variables tf.global_variables_initializer().run() for i in range(): for start, end in zip(range(, len(trX), ), range(, len(trX)+, )): sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]}) print(i, np.mean(np.argmax(teY, axis=) == sess.run(predict_op, feed_dict={X: teX})))import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_datadef init_weights (shape) : return tf.Variable(tf.random_normal(shape, stddev= 0.01 )) def model (X, w_h, w_o) : h = tf.nn.sigmoid(tf.matmul(X, w_h)) # this is a basic mlp, think 2 stacked logistic regressions return tf.matmul(h, w_o) # note that we dont take the softmax at the end because our cost fn does that for us mnist = input_data.read_data_sets( "MNIST_data/" , one_hot= True )trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labelsX = tf.placeholder( "float" , [ None , ])Y = tf.placeholder( "float" , [ None , ])w_h = init_weights([, ]) # create symbolic variables w_o = init_weights([, ])py_x = model(X, w_h, w_o)cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y)) # compute costs train_op = tf.train.GradientDescentOptimizer( 0.05 ).minimize(cost) # construct an optimizer predict_op = tf.argmax(py_x, )# Launch the graph in a session with tf.Session() as sess: # you need to initialize all variables tf.global_variables_initializer().run() for i in range(): for start, end in zip(range(, len(trX), ), range(, len(trX)+, )): sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]}) print(i, np.mean(np.argmax(teY, axis=) == sess.run(predict_op, feed_dict={X: teX})))import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_datadef init_weights (shape) : return tf.Variable(tf.random_normal(shape, stddev= 0.01 )) def model (X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden) : # this network is the same as the previous one except with an extra hidden layer + dropout X = tf.nn.dropout(X, p_keep_input) h = tf.nn.relu(tf.matmul(X, w_h)) h = tf.nn.dropout(h, p_keep_hidden) h2 = tf.nn.relu(tf.matmul(h, w_h2)) h2 = tf.nn.dropout(h2, p_keep_hidden) return tf.matmul(h2, w_o)mnist = input_data.read_data_sets( "MNIST_data/" , one_hot= True )trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labelsX = tf.placeholder( "float" , [ None , ])Y = tf.placeholder( "float" , [ None , ])w_h = init_weights([, ])w_h2 = init_weights([, ])w_o = init_weights([, ])p_keep_input = tf.placeholder( "float" )p_keep_hidden = tf.placeholder( "float" )py_x = model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden)cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))train_op = tf.train.RMSPropOptimizer( 0.001 , 0.9 ).minimize(cost)predict_op = tf.argmax(py_x, )# Launch the graph in a session with tf.Session() as sess: # you need to initialize all variables tf.global_variables_initializer().run() for i in range(): for start, end in zip(range(, len(trX), ), range(, len(trX)+, )): sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end], p_keep_input: 0.8 , p_keep_hidden: 0.5 }) print(i, np.mean(np.argmax(teY, axis=) == sess.run(predict_op, feed_dict={X: teX, Y: teY, p_keep_input: 1.0 , p_keep_hidden: 1.0 })))import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_databatch_size = test_size = def init_weights (shape) : return tf.Variable(tf.random_normal(shape, stddev= 0.01 )) def model (X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden) : l1a = tf.nn.relu(tf.nn.conv2d(X, w, # l1a shape=(?, 28, 28, 32) strides=[, , , ], padding= 'SAME' )) l1 = tf.nn.max_pool(l1a, ksize=[, , , ], # l1 shape=(?, 14, 14, 32) strides=[, , , ], padding= 'SAME' ) l1 = tf.nn.dropout(l1, p_keep_conv) l2a = tf.nn.relu(tf.nn.conv2d(l1, w2, # l2a shape=(?, 14, 14, 64) strides=[, , , ], padding= 'SAME' )) l2 = tf.nn.max_pool(l2a, ksize=[, , , ], # l2 shape=(?, 7, 7, 64) strides=[, , , ], padding= 'SAME' ) l2 = tf.nn.dropout(l2, p_keep_conv) l3a = tf.nn.relu(tf.nn.conv2d(l2, w3, # l3a shape=(?, 7, 7, 128) strides=[, , , ], padding= 'SAME' )) l3 = tf.nn.max_pool(l3a, ksize=[, , , ], # l3 shape=(?, 4, 4, 128) strides=[, , , ], padding= 'SAME' ) l3 = tf.reshape(l3, [ -1 , w4.get_shape().as_list()[]]) # reshape to (?, 2048) l3 = tf.nn.dropout(l3, p_keep_conv) l4 = tf.nn.relu(tf.matmul(l3, w4)) l4 = tf.nn.dropout(l4, p_keep_hidden) pyx = tf.matmul(l4, w_o) return pyxmnist = input_data.read_data_sets( "MNIST_data/" , one_hot= True )trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labelstrX = trX.reshape( -1 , , , ) # 28x28x1 input img teX = teX.reshape( -1 , , , ) # 28x28x1 input imgFont size:
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