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2abc87c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | # Old Code in Tensorflow v1
import tensorflow as tf
# Define a computation graph
graph = tf.Graph()
with graph.as_default():
input_data = tf.placeholder(tf.float32, shape=(None, input_size), name="input_data")
target_data = tf.placeholder(tf.float32, shape=(None, output_size), name="target_data")
hidden_layer = tf.layers.dense(inputs=input_data, units=64, activation=tf.nn.relu, name="hidden_layer")
output_layer = tf.layers.dense(inputs=hidden_layer, units=output_size, name="output_layer")
loss = tf.reduce_mean(tf.square(output_layer - target_data), name="loss")
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(loss, name="train_op")
with tf.Session(graph=graph) as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(num_epochs):
_, current_loss = sess.run([train_op, loss], feed_dict={input_data: train_input, target_data: train_target})
print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {current_loss}")
predicted_output = sess.run(output_layer, feed_dict={input_data: test_input})
# Updated code in Tensorflow v2
import tensorflow as tf
import numpy as np
# Define the model architecture using the Sequential API
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(input_size,), name='hidden_layer'),
tf.keras.layers.Dense(output_size, name='output_layer')
])
# Compile the model
model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01), loss='mean_squared_error')
# Train the model
history = model.fit(train_input, train_target, epochs=num_epochs, batch_size=batch_size, verbose=1)
# Make predictions
predicted_output = model.predict(test_input)
# Print the training history
print("Training history:")
print(history.history)
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