Few examples of Machine Learning Deep Neural Network Applications in Python with source code for your projects
Harisha Lakshan Warnakulasuriya
Senior Software Engineer | Designing Innovative Technology for Industrial Sectors
A deep neural network (DNN) is a type of artificial neural network that has multiple layers between the input and output layers. Each layer consists of a set of neurons that perform computations on the input data and pass the output to the next layer. The output of the final layer represents the network's prediction for a given input.
The idea behind deep neural networks is to learn hierarchical representations of the input data. Each layer in the network learns to represent the input data at a different level of abstraction. The lower layers learn simple features, such as edges and corners, while the higher layers learn more complex features that are combinations of the lower-level features.
Here's an example of a deep neural network architecture:
Input layer -> Hidden layer 1 -> Hidden layer 2 -> Output layer
The input layer receives the input data and passes it to the first hidden layer. Each neuron in the hidden layer performs a weighted sum of the inputs and applies an activation function to the result. The output of the first hidden layer is then passed to the second hidden layer, where the same computations are performed. Finally, the output of the second hidden layer is passed to the output layer, which produces the final output of the network.
The weights and biases of the neurons in the network are learned during training using an optimization algorithm such as stochastic gradient descent. During training, the network tries to minimize a loss function that measures how well the network's predictions match the true labels. By adjusting the weights and biases of the neurons, the network learns to make better predictions on the training data.
In Python, deep neural networks can be implemented using various libraries such as Keras, TensorFlow, and PyTorch. These libraries provide a high-level API for building, training, and evaluating neural networks. To build a deep neural network in Python, you typically define the network architecture using the library's API, compile the network with a loss function and an optimizer, and then train the network on a dataset. After training, you can evaluate the performance of the network on a separate test set or use it to make predictions on new data.
an example of building a deep neural network classifier in Python using the Keras library:
import numpy as n
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense, Dropout
# Load the dataset
data = pd.read_csv('dataset.csv')
# Split the dataset into training and testing sets
X = data.drop('target', axis=1)
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Define the model architecture
model = Sequential()
model.add(Dense(32, input_dim=X_train.shape[1], activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(16, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
# Compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Train the model
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))
# Evaluate the model on the testing set
score = model.evaluate(X_test, y_test)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
In this example, we first load the dataset and split it into training and testing sets using the `train_test_split` function from the `sklearn` library. We then define the model architecture using the `Sequential` class from Keras and add three fully connected layers with the `Dense` class. We also add dropout layers to reduce overfitting. Finally, we compile the model with the binary cross-entropy loss function and the Adam optimizer and train the model for 50 epochs. We evaluate the model on the testing set and print out the test loss and accuracy.
an example of how to implement a Deep Neural Network (DNN) classifier in Python using TensorFlow:
import tensorflow as t
# Define the input and output sizes
input_size = 784
output_size = 10
# Define the number of neurons in each hidden layer
hidden_layer1 = 256
hidden_layer2 = 128
hidden_layer3 = 64
# Define the learning rate and number of training epochs
learning_rate = 0.001
epochs = 100
# Define the training data and labels
train_data, train_labels = ...
# Define the testing data and labels
test_data, test_labels = ...
# Define the TensorFlow graph
graph = tf.Graph()
with graph.as_default():
? ? # Define the input placeholder
? ? x = tf.placeholder(tf.float32, shape=[None, input_size])
? ? y = tf.placeholder(tf.float32, shape=[None, output_size])
? ??
? ? # Define the first hidden layer
? ? w1 = tf.Variable(tf.truncated_normal([input_size, hidden_layer1], stddev=0.1))
? ? b1 = tf.Variable(tf.constant(0.1, shape=[hidden_layer1]))
? ? h1 = tf.nn.relu(tf.matmul(x, w1) + b1)
? ??
? ? # Define the second hidden layer
? ? w2 = tf.Variable(tf.truncated_normal([hidden_layer1, hidden_layer2], stddev=0.1))
? ? b2 = tf.Variable(tf.constant(0.1, shape=[hidden_layer2]))
? ? h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)
? ??
? ? # Define the third hidden layer
? ? w3 = tf.Variable(tf.truncated_normal([hidden_layer2, hidden_layer3], stddev=0.1))
? ? b3 = tf.Variable(tf.constant(0.1, shape=[hidden_layer3]))
? ? h3 = tf.nn.relu(tf.matmul(h2, w3) + b3)
? ??
? ? # Define the output layer
? ? w4 = tf.Variable(tf.truncated_normal([hidden_layer3, output_size], stddev=0.1))
? ? b4 = tf.Variable(tf.constant(0.1, shape=[output_size]))
? ? logits = tf.matmul(h3, w4) + b4
? ??
? ? # Define the loss function and optimizer
? ? loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
? ? optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)
? ??
? ? # Define the accuracy metric
? ? correct_predictions = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
? ? accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
? ??
# Train the model
with tf.Session(graph=graph) as sess:
? ? sess.run(tf.global_variables_initializer())
? ??
? ? for epoch in range(epochs):
? ? ? ? _, batch_loss, batch_accuracy = sess.run([optimizer, loss, accuracy], feed_dict={x: train_data, y: train_labels})
? ? ? ??
? ? ? ? print("Epoch:", epoch+1, "Training Loss:", batch_loss, "Training Accuracy:", batch_accuracy)
? ??
? ? # Evaluate the model on the testing data
? ? test_accuracy = sess.run(accuracy, feed_dict={x: test_data, y: test_labels})
? ? print("Test Accuracy:", test_accuracy)
In this example, we define a DNN with three hidden layers and train it on the MNIST dataset. The input size is 784 (28x28 pixels) and the output size is 10 (one-hot encoded labels). We use the ReLU activation function in the hidden layers and the softmax function in the output layer. We use the Adam optimizer and the cross-entropy loss
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