Using Tensoflow 2.6.2

Hello, I am new to Tensorflow, and on a LinkedIn course, I am trying to run this file:
import os
import tensorflow as tf
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

Turn off TensorFlow warning messages in program output

os.environ[‘TF_CPP_MIN_LOG_LEVEL’] = ‘2’

Load training data set from CSV file

training_data_df = pd.read_csv(“sales_data_training.csv”, dtype=float)

Pull out columns for X (data to train with) and Y (value to predict)

X_training = training_data_df.drop(‘total_earnings’, axis=1).values
Y_training = training_data_df[[‘total_earnings’]].values

Load testing data set from CSV file

test_data_df = pd.read_csv(“sales_data_test.csv”, dtype=float)

Pull out columns for X (data to train with) and Y (value to predict)

X_testing = test_data_df.drop(‘total_earnings’, axis=1).values
Y_testing = test_data_df[[‘total_earnings’]].values

All data needs to be scaled to a small range like 0 to 1 for the neural

network to work well. Create scalers for the inputs and outputs.

X_scaler = MinMaxScaler(feature_range=(0, 1))
Y_scaler = MinMaxScaler(feature_range=(0, 1))

Scale both the training inputs and outputs

X_scaled_training = X_scaler.fit_transform(X_training)
Y_scaled_training = Y_scaler.fit_transform(Y_training)

It’s very important that the training and test data are scaled with the same scaler.

X_scaled_testing = X_scaler.transform(X_testing)
Y_scaled_testing = Y_scaler.transform(Y_testing)

Define model parameters

learning_rate = 0.001
training_epochs = 100

Define how many inputs and outputs are in our neural network

number_of_inputs = 9
number_of_outputs = 1

Define how many neurons we want in each layer of our neural network

layer_1_nodes = 50
layer_2_nodes = 100
layer_3_nodes = 50

Input Layer

with tf.variable_scope(‘input’):
X = tf.placeholder(tf.float32, shape=(None, number_of_inputs))

Layer 1

with tf.variable_scope(‘layer_1’):

weights = tf.get_variable("weights1", shape=[number_of_inputs, layer_1_nodes], initializer=tf.contrib.layers.xavier_initializer())
biases = tf.get_variable(name="biases1", shape=[layer_1_nodes], initializer=tf.zeros_initializer())
layer_1_output = tf.nn.relu(tf.matmul(X, weights) + biases)

Layer 2

with tf.variable_scope(‘layer_2’):
weights = tf.get_variable(“weights2”, shape=[layer_1_nodes, layer_2_nodes], initializer=tf.contrib.layers.xavier_initializer())
biases = tf.get_variable(name=“biases2”, shape=[layer_2_nodes], initializer=tf.zeros_initializer())
layer_2_output = tf.nn.relu(tf.matmul(layer_1_output, weights) + biases)

Layer 3

with tf.variable_scope(‘layer_3’):
weights = tf.get_variable(“weights3”, shape=[layer_2_nodes, layer_3_nodes], initializer=tf.contrib.layers.xavier_initializer())
biases = tf.get_variable(name=“biases3”, shape=[layer_3_nodes], initializer=tf.zeros_initializer())
layer_3_output = tf.nn.relu(tf.matmul(layer_2_output, weights) + biases)

Output Layer

with tf.variable_scope(‘output’):
weights = tf.get_variable(“weights4”, shape=[layer_3_nodes, number_of_outputs], initializer=tf.contrib.layers.xavier_initializer())
biases = tf.get_variable(name=“biases4”, shape=[number_of_outputs], initializer=tf.zeros_initializer())
prediction = tf.matmul(layer_3_output, weights) + biases

Section Two: Define the cost function of the neural network that will measure prediction accuracy during training

with tf.variable_scope(‘cost’):
Y = tf.placeholder(tf.float32, shape=(None, 1))
cost = tf.reduce_mean(tf.squared_difference(prediction, Y))

Section Three: Define the optimizer function that will be run to optimize the neural network

with tf.variable_scope(‘train’):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)

Initialize a session so that we can run TensorFlow operations

with tf.Session() as session:

# Run the global variable initializer to initialize all variables and layers of the neural network

# Run the optimizer over and over to train the network.
# One epoch is one full run through the training data set.
for epoch in range(training_epochs):

    # Feed in the training data and do one step of neural network training, feed_dict={X: X_scaled_training, Y: Y_scaled_training})

    # Every 5 training steps, log our progress

# Training is now complete!
print("Training is complete!")

However, I’ve been receiving AttributeErrors, apparently due to the wrong version of tensorflow I am using. Does anyone know which tensorflow functions I must write?

Thank you.

Hi @Oleg_Ananiev, As you said you are using tensorflow 2.6 version but the code you mentioned is written using the 1.x version of tensorflow. Which is not compatible. I request you to refer to this migration document to migrate your code 2.x version. Thank You.