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# rajsandhu1989/2.py

Last active Feb 10, 2022
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 #import tensorflow as tf import tensorflow as tf # Declare a constant, we need to import constant from tensorflow sample_constant=tf.constant(20) print(sample_constant.dtype) # Now we can perform some operations using tensorlfow in this constant #Similarly, we can create variables also A1=tf.Variable([1,2,3,4]) print(A1) #Above created variable can be printed using the numpy conversion print(A1.numpy()) B1=A1.numpy() B1=tf.Variable(B1) print(B1)
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 #create two tensors a1=tf.fill([3,3], 7) a2=tf.fill([3,3], 3) #Add the two tensors a3=tf.add(a1,a2) #print final tensor print(a3.numpy())
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 #fill method c33=tf.fill([3,3], 7) print(c33.numpy())
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 #Ones function A1=tf.ones([3,2], tf.int32) print(A1.numpy()) #Zeros function B1=tf.zeros([3,2]) print(B1) A1=tf.constant([1,2,3,4]) A23=tf.constant([[1,2,3], [4,5,6]]) #create ones tensor and perform element wise multiplication B1=tf.ones_like(A1) B23=tf.ones_like(A23)
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 # Define x x = tf.Variable(6.0) # Define y within instance of GradientTape with tf.GradientTape() as gt: gt.watch(x) y = tf.multiply(x, x) #Evaluate the gradient of y at x = 6 g = gt.gradient(y, x) print(g.numpy()) ''' OUTPUT 12.0 '''
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 #extract mpg and horsepower from the dataset mpg_1=np.array(mpg['mpg'], np.float32) horsepower=np.array(mpg['horsepower'], np.float32) #define the intercept and slope intercept=tf.Variable(0.2, np.float32) slope=tf.Variable(0.2, np.float32) #create a linear regression using y=mx+b def linear_regression(intercept, slope, features=horsepower): return slope*features+intercept #create a loss function def loss_function(intercept, slope, target=mpg_1, features=horsepower): #create predictions pred=linear_regression(intercept, slope, features) loss= tf.keras.losses.mse(target, pred) return loss #create an instance of optimizer opt=tf.keras.optimizers.Adam() #minimize the loss using epochs epochs=1000 for i in range(epochs): opt.minimize(lambda: loss_function(intercept, slope), var_list=[intercept, slope]) print(np.array(loss_function(intercept, slope))) print(np.array(intercept), np.array(slope))
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 #Ones function A1=tf.ones([3,2], tf.int32) #create ones tensor and perform element wise multiplication B1=tf.ones_like(A1) C1=tf.multiply(A1,B1) # Matrix multiplication #create feature value feat_value=tf.constant([[1,12],[2,13],[3,14]]) '''' shape is tf.Tensor( [[ 1 12] [ 2 13] [ 3 14]], shape=(3, 2), dtype=int32)''' parameters=tf.constant([,]) ''' Shape is tf.Tensor( [ ], shape=(2, 1), dtype=int32)''' #create predictions by matrix multiplication pred=tf.matmul(feat_value, parameters) print(pred)
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 #we can reduce the value of tensor feat_value=tf.constant([[1,12],[2,13],[3,14]]) pred=tf.reduce_sum(feat_value) print(pred) ''' OUTPUT tf.Tensor(45, shape=(), dtype=int32) ''' #we can reduce at any dimension also print(tf.reduce_sum(feat_value, 0)) # This is reduce sum for zero dimension means column wise ''' OUTPUT tf.Tensor([ 6 39], shape=(2,), dtype=int32) ''' print(tf.reduce_sum(feat_value, 1)) # This is reduce sum for first dimension means row wise ''' OUTPUT tf.Tensor([13 15 17], shape=(3,), dtype=int32) '''
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 #we need to reshape a picture so that it can be feed to neural network #lets say we have a 28*28 grayscale image image=tf.random.uniform([28,28], maxval=255, dtype='int32') image_reshape=tf.reshape(image, [28*28,1]) #this is required when we input an image to the neural network
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reduce_all() Computes Logical AND across dimensions of tensors. reduce_any() Computes Logical OR across dimensions of tensors. reduce_euclidean_norm() Computes the Euclidean norm of elements across dimensions of a tensor. reduce_max() Finds maximum across tensor dimension reduce_min() Finds minimum across tensor dimension reduce_mean() Computes the mean of elements across dimensions of a tensor. reduce_prod() Computes the products of elements across dimensions of a tensor. reduce_std() Computes the standard deviation of elements across dimensions of a tensor.
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