1dcnn python

1D CNN (Convolutional Neural Network) in Python is a type of neural network commonly used for analyzing sequential data, such as time series or text data. It is similar to the traditional 2D CNN used in image classification but operates on one-dimensional input.

To implement a 1D CNN in Python, you can use frameworks like TensorFlow or Keras. Here's an example code snippet using Keras:

from keras.models import Sequential
from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense

# Create a sequential model
model = Sequential()

# Add a 1D convolutional layer
model.add(Conv1D(64, kernel_size=3, activation='relu', input_shape=(input_length, input_dim)))

# Add a max pooling layer
model.add(MaxPooling1D(pool_size=2))

# Flatten the output from the previous layer
model.add(Flatten())

# Add a fully connected layer
model.add(Dense(64, activation='relu'))

# Add the output layer
model.add(Dense(num_classes, activation='softmax'))

# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=32)

# Evaluate the model
score = model.evaluate(X_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

In this example, we build a simple 1D CNN model with a convolutional layer, max pooling layer, flatten layer, and fully connected layers. The input shape is specified as `(input_length, input_dim)`, where `input_length` represents the length of the input sequence and `input_dim` represents the number of features at each time step.

Remember to preprocess your data accordingly and adjust the hyperparameters (e.g., kernel size, number of filters, etc.) based on your specific problem.