3-2 Mid-level API: Demonstration

The examples below use mid-level APIs in TensorFlow to implement a linear regression model and a DNN binary classification model.

Mid-level API includes model layers, loss functions, optimizers, data pipelines, feature columns, etc.

import tensorflow as tf
# Time stamp
@tf.function
def printbar():
    today_ts = tf.timestamp()%(24*60*60)
    hour = tf.cast(today_ts//3600+8,tf.int32)%tf.constant(24)
    minite = tf.cast((today_ts%3600)//60,tf.int32)
    second = tf.cast(tf.floor(today_ts%60),tf.int32)
    def timeformat(m):
        if tf.strings.length(tf.strings.format("{}",m))==1:
            return(tf.strings.format("0{}",m))
        else:
            return(tf.strings.format("{}",m))
    timestring = tf.strings.join([timeformat(hour),timeformat(minite),
                timeformat(second)],separator = ":")
    tf.print("=========="*8+timestring)

1. Linear Regression Model

(a) Data Preparation

import numpy as np 
import pandas as pd
from matplotlib import pyplot as plt 
import tensorflow as tf
from tensorflow.keras import layers,losses,metrics,optimizers
# Number of sample
n = 400
# Generating the datasets
X = tf.random.uniform([n,2],minval=-10,maxval=10) 
w0 = tf.constant([[2.0],[-3.0]])
b0 = tf.constant([[3.0]])
Y = X@w0 + b0 + tf.random.normal([n,1],mean = 0.0,stddev= 2.0)  # @ is matrix multiplication; adding Gaussian noise

# Data Visualization
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
plt.figure(figsize = (12,5))
ax1 = plt.subplot(121)
ax1.scatter(X[:,0],Y[:,0], c = "b")
plt.xlabel("x1")
plt.ylabel("y",rotation = 0)
ax2 = plt.subplot(122)
ax2.scatter(X[:,1],Y[:,0], c = "g")
plt.xlabel("x2")
plt.ylabel("y",rotation = 0)
plt.show()

# Creating generator of data pipeline
ds = tf.data.Dataset.from_tensor_slices((X,Y)) \
     .shuffle(buffer_size = 100).batch(10) \
     .prefetch(tf.data.experimental.AUTOTUNE)

(b) Model Definition

model = layers.Dense(units = 1) 
model.build(input_shape = (2,)) #Creating variables using the build method
model.loss_func = losses.mean_squared_error
model.optimizer = optimizers.SGD(learning_rate=0.001)

(c) Model Training

# Accelerate using Autograph to transform the dynamic graph into static
@tf.function
def train_step(model, features, labels):
    with tf.GradientTape() as tape:
        predictions = model(features)
        loss = model.loss_func(tf.reshape(labels,[-1]), tf.reshape(predictions,[-1]))
    grads = tape.gradient(loss,model.variables)
    model.optimizer.apply_gradients(zip(grads,model.variables))
    return loss
# Testing the results of train_step
features,labels = next(ds.as_numpy_iterator())
train_step(model,features,labels)

def train_model(model,epochs):
    for epoch in tf.range(1,epochs+1):
        loss = tf.constant(0.0)
        for features, labels in ds:
            loss = train_step(model,features,labels)
        if epoch%50==0:
            printbar()
            tf.print("epoch =",epoch,"loss = ",loss)
            tf.print("w =",model.variables[0])
            tf.print("b =",model.variables[1])
train_model(model,epochs = 200)

================================================================================17:01:48
epoch = 50 loss =  2.56481647
w = [[1.99355531]
 [-2.99061537]]
b = [3.09484935]
================================================================================17:01:51
epoch = 100 loss =  5.96198225
w = [[1.98028314]
 [-2.96975136]]
b = [3.09501529]
================================================================================17:01:54
epoch = 150 loss =  4.79625702
w = [[2.00056171]
 [-2.98774862]]
b = [3.09567738]
================================================================================17:01:58
epoch = 200 loss =  8.26704407
w = [[2.00282311]
 [-2.99300027]]
b = [3.09406662]

# Visualizing the results
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
w,b = model.variables
plt.figure(figsize = (12,5))
ax1 = plt.subplot(121)
ax1.scatter(X[:,0],Y[:,0], c = "b",label = "samples")
ax1.plot(X[:,0],w[0]*X[:,0]+b[0],"-r",linewidth = 5.0,label = "model")
ax1.legend()
plt.xlabel("x1")
plt.ylabel("y",rotation = 0)
ax2 = plt.subplot(122)
ax2.scatter(X[:,1],Y[:,0], c = "g",label = "samples")
ax2.plot(X[:,1],w[1]*X[:,1]+b[0],"-r",linewidth = 5.0,label = "model")
ax2.legend()
plt.xlabel("x2")
plt.ylabel("y",rotation = 0)
plt.show()

2. DNN Binary Classification Model

(a) Data Preparation

import numpy as np 
import pandas as pd 
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers,losses,metrics,optimizers
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
## Number of the positive/negative samples
n_positive,n_negative = 2000,2000
# Generating the positive samples with a distribution on a smaller ring
r_p = 5.0 + tf.random.truncated_normal([n_positive,1],0.0,1.0)
theta_p = tf.random.uniform([n_positive,1],0.0,2*np.pi) 
Xp = tf.concat([r_p*tf.cos(theta_p),r_p*tf.sin(theta_p)],axis = 1)
Yp = tf.ones_like(r_p)
# Generating the negative samples with a distribution on a larger ring
r_n = 8.0 + tf.random.truncated_normal([n_negative,1],0.0,1.0)
theta_n = tf.random.uniform([n_negative,1],0.0,2*np.pi) 
Xn = tf.concat([r_n*tf.cos(theta_n),r_n*tf.sin(theta_n)],axis = 1)
Yn = tf.zeros_like(r_n)
# Assembling all samples
X = tf.concat([Xp,Xn],axis = 0)
Y = tf.concat([Yp,Yn],axis = 0)
# Visualizing the data
plt.figure(figsize = (6,6))
plt.scatter(Xp[:,0].numpy(),Xp[:,1].numpy(),c = "r")
plt.scatter(Xn[:,0].numpy(),Xn[:,1].numpy(),c = "g")
plt.legend(["positive","negative"]);

# Create pipeline for the input data
ds = tf.data.Dataset.from_tensor_slices((X,Y)) \
     .shuffle(buffer_size = 4000).batch(100) \
     .prefetch(tf.data.experimental.AUTOTUNE)

(b) Model Definition

class DNNModel(tf.Module):
    def __init__(self,name = None):
        super(DNNModel, self).__init__(name=name)
        self.dense1 = layers.Dense(4,activation = "relu") 
        self.dense2 = layers.Dense(8,activation = "relu")
        self.dense3 = layers.Dense(1,activation = "sigmoid")
    # Forward propagation
    @tf.function(input_signature=[tf.TensorSpec(shape = [None,2], dtype = tf.float32)])  
    def __call__(self,x):
        x = self.dense1(x)
        x = self.dense2(x)
        y = self.dense3(x)
        return y
model = DNNModel()
model.loss_func = losses.binary_crossentropy
model.metric_func = metrics.binary_accuracy
model.optimizer = optimizers.Adam(learning_rate=0.001)

# Testing the structure of model
(features,labels) = next(ds.as_numpy_iterator())
predictions = model(features)
loss = model.loss_func(tf.reshape(labels,[-1]),tf.reshape(predictions,[-1]))
metric = model.metric_func(tf.reshape(labels,[-1]),tf.reshape(predictions,[-1]))
tf.print("init loss:",loss)
tf.print("init metric",metric)

init loss: 1.13653195
init metric 0.5

(c) Model Training

# Transform to static graph for acceleration using Autograph
@tf.function
def train_step(model, features, labels):
    with tf.GradientTape() as tape:
        predictions = model(features)
        loss = model.loss_func(tf.reshape(labels,[-1]), tf.reshape(predictions,[-1]))
    grads = tape.gradient(loss,model.trainable_variables)
    model.optimizer.apply_gradients(zip(grads,model.trainable_variables))
    metric = model.metric_func(tf.reshape(labels,[-1]), tf.reshape(predictions,[-1]))
    return loss,metric
# Testing the result of train_step
features,labels = next(ds.as_numpy_iterator())
train_step(model,features,labels)

(<tf.Tensor: shape=(), dtype=float32, numpy=1.2033114>,
 <tf.Tensor: shape=(), dtype=float32, numpy=0.47>)

@tf.function
def train_model(model,epochs):
    for epoch in tf.range(1,epochs+1):
        loss, metric = tf.constant(0.0),tf.constant(0.0)
        for features, labels in ds:
            loss,metric = train_step(model,features,labels)
        if epoch%10==0:
            printbar()
            tf.print("epoch =",epoch,"loss = ",loss, "accuracy = ",metric)
train_model(model,epochs = 60)

================================================================================17:07:36
epoch = 10 loss =  0.556449413 accuracy =  0.79
================================================================================17:07:38
epoch = 20 loss =  0.439187407 accuracy =  0.86
================================================================================17:07:40
epoch = 30 loss =  0.259921253 accuracy =  0.95
================================================================================17:07:42
epoch = 40 loss =  0.244920313 accuracy =  0.9
================================================================================17:07:43
epoch = 50 loss =  0.19839409 accuracy =  0.92
================================================================================17:07:45
epoch = 60 loss =  0.126151696 accuracy =  0.95

# Visualizing the results
fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize = (12,5))
ax1.scatter(Xp[:,0].numpy(),Xp[:,1].numpy(),c = "r")
ax1.scatter(Xn[:,0].numpy(),Xn[:,1].numpy(),c = "g")
ax1.legend(["positive","negative"]);
ax1.set_title("y_true");
Xp_pred = tf.boolean_mask(X,tf.squeeze(model(X)>=0.5),axis = 0)
Xn_pred = tf.boolean_mask(X,tf.squeeze(model(X)<0.5),axis = 0)
ax2.scatter(Xp_pred[:,0].numpy(),Xp_pred[:,1].numpy(),c = "r")
ax2.scatter(Xn_pred[:,0].numpy(),Xn_pred[:,1].numpy(),c = "g")
ax2.legend(["positive","negative"]);
ax2.set_title("y_pred");

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