Delve into the world of Keras, a powerful deep learning API within the Python DL framework, and discover its extensive capabilities for building neural network layers efficiently.
pip install kerasWhat is keras and why use it?
Key features and capabilities
Installation instructions
Basic usage examples
Common use cases
Best practices and tips
import keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense\n\n# Initialize the neural network\ndef create_model():\n model = Sequential()\n model.add(Dense(12, input_dim=8, activation='relu'))\n model.add(Dense(8, activation='relu'))\n model.add(Dense(1, activation='sigmoid'))\n return model\n\nmodel = create_model()\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
from keras.models import Model\nfrom keras.layers import Input, Dense, Dropout\n\n# Define the input\ninput_layer = Input(shape=(64,))\n\n# Add layers\nx = Dense(128, activation='relu')(input_layer)\nx = Dropout(0.5)(x)\nx = Dense(64, activation='relu')(x)\n\n# Define the output\noutput_layer = Dense(10, activation='softmax')(x)\n\n# Create the model\nmodel = Model(inputs=input_layer, outputs=output_layer)\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
create_modelBuilds a simple neural network model with Keras