pip install tensorflow?import tensorflow.keras as keras?pip install tensorflow==2.7.0?import tensorflow.keras as keras
# Initialize a Sequential model
model = keras.Sequential()
# Add layers to the model
model.add(keras.layers.Dense(units=64, activation='relu', input_shape=(784,))) # Input layer
model.add(keras.layers.Dense(units=128, activation='relu')) # Hidden layer
model.add(keras.layers.Dense(units=10, activation='softmax')) # Output layer
# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Print model summary
model.summary()?tensorflow.keras).keras.Sequential().add() method. We add a Dense layer as the input layer with 64 units, ReLU activation function, and input shape (784,). Then, we add another Dense layer as a hidden layer with 128 units and ReLU activation function. Finally, we add a Dense layer as the output layer with 10 units (assuming it's a classification task) and softmax activation function.compile() method, where we specify the optimizer (in this case, 'adam'), the loss function (categorical crossentropy for multi-class classification), and the evaluation metric ('accuracy').summary() method, which provides information about the layers, output shapes, and parameters of the model. import keras
from keras import layers
# Initialize a sequential model
model = keras.Sequential()
# Add layers one by one
model.add(layers.Dense(64, activation='relu', input_shape=(784,)))
model.add(layers.Dense(10, activation='softmax'))?import keras
from keras import layers
# Input tensor placeholder
inputs = keras.Input(shape=(784,))
# Hidden layers
x = layers.Dense(64, activation='relu')(inputs)
x = layers.Dense(64, activation='relu')(x)
# Output layer for 10-class classification
outputs = layers.Dense(10, activation='softmax')(x)
# Define the model
model = keras.Model(inputs=inputs, outputs=outputs)?import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# Define input for numerical data
numerical_input = keras.Input(shape=(2,), name="numerical_input")
# Define input for categorical data
categorical_input = keras.Input(shape=(3,), name="categorical_input")
# Multi-modal input concatenation
concatenation = layers.Concatenate()([numerical_input, categorical_input])
# Hidden layer
hidden1 = layers.Dense(3, activation='relu')(concatenation)
# Define two output branches from the hidden layer
output1 = layers.Dense(1, name="output1")(hidden1)
output2 = layers.Dense(1, name="output2")(hidden1)
# Create the model
model = keras.Model(inputs=[numerical_input, categorical_input], outputs=[output1, output2])
# Compile the model
model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.01),
loss={"output1": "mse", "output2": "mse"},
metrics={"output1": "mae", "output2": "mae"})?pip or conda. Before installation, make sure you have Python >= 3.5, as Keras does not support Python 2.pip :pip install tensorflow # Install TensorFlow backend as Keras may not work without TensorFlow.?
pip install kerasconda (recommended with Anaconda distribution) :conda install -c conda-forge keras
conda install -c conda-forge tensorflow?import os
os.environ['KERAS_BACKEND'] = 'tensorflow'?import keras
from keras import backend as K
print(keras.__version__) # Display Keras version.
print(K.backend()) # Display the current backend (e.g., 'tensorflow').?fit(). Callbacks are objects that get called by the model at different points during training like: Firstly, at the beginning and end of each batch Secondly, at the beginning and end of each epoch However, callbacks are a way to make model trainable entirely scriptable. This can be used for periodically saving your model. from keras.models import model_from_json
# Save model in JSON format (architecture-only)
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# Load model from JSON
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# Save model in HDF5 format (stateful model)
model.save("model.h5")
# Load model from HDF5
loaded_model_h5 = load_model('model.h5')?fit() function in Keras is used to train a neural network model on a given dataset. It takes the training data, validation data (optional), batch size, number of epochs, and other optional parameters as input and iterates over the training data for the specified number of epochs, updating the model's parameters (weights) based on the optimization algorithm and the specified loss function.fit() function and how it works:fit() function is called, Keras iterates over the training data for the specified number of epochs, updating the model's parameters using the specified optimization algorithm and loss function. After each epoch, the model's performance on the training and validation data (if provided) is evaluated and displayed, and the training progress is logged based on the specified verbosity level. Once training is complete, the trained model parameters are returned. compile() function. They are used to monitor the model's performance during training and can be displayed in the training output to track progress.ImageDataGenerator class.
SimpleRNN, LSTM (Long Short-Term Memory), or GRU (Gated Recurrent Unit) layers. Here's how you can implement an RNN in Keras:from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import SimpleRNN, LSTM, GRU, Dense
# Define the model architecture
model = Sequential()
# Add an RNN layer (e.g., SimpleRNN, LSTM, GRU)
model.add(SimpleRNN(units=64, input_shape=(time_steps, input_dim))) # SimpleRNN
# model.add(LSTM(units=64, input_shape=(time_steps, input_dim))) # LSTM
# model.add(GRU(units=64, input_shape=(time_steps, input_dim))) # GRU
# Add a dense output layer
model.add(Dense(units=num_classes, activation='softmax'))
# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Print the model summary
model.summary()?Sequential().SimpleRNN, LSTM, or GRU) to the model using add(). Each of these layers takes the units parameter, which specifies the dimensionality of the output space, and the input_shape parameter, which specifies the shape of the input data.add(). This layer is typically used to produce the final predictions or outputs of the model.compile() with the appropriate optimizer, loss function, and metrics.summary() to display the architecture of the model, including the number of parameters and output shapes of each layer.