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Training of a Keras model Numpy arrays of input data and labels are used to train the Keras models. For training Fit function is used. For a single-input model with 2 classes (binary classification): model = Sequential() model.add(Dense(32, activation='relu', input_dim=100)) model.add(Dense(1, activation='sigmoid')) model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) # Generate dummy data import numpy as np data = np.random.random((1000, 100)) labels = np.random.randint(2, size=(1000, 1)) # Train the model, iterating on the data in batches of 32 samples model.fit(data, labels, epochs=10, batch_size=32) For a single-input model with 10 classes (categorical classification): model = Sequential() model.add(Dense(32, activation='relu', input_dim=100)) model.add(Dense(10, activation='softmax')) model.compile(optimizer=...
Keras Sequential Model Creating the model | Specifying input size and batch size | specifying the activation function Sequential Model : Linear stack of layers. Create a Sequential Model by passing the layer instances to the constructor. Import sequential model from Keras models. from keras.models import Sequential Import dense layer and activation function from keras layers from keras.layers import Dense, Activation Create sequential model Input = can be of any dimension or a single element Shape = length along each dimension of the input Model needs to know the input shape it expects. Only the first layer of the sequential model should know its input shape. Batch = collection of inputs Batch Size = no of inputs Create the model model = Sequential() Create the layer with batch size of 32 and input size of 784 Dense(32,input_dim=784) Add the la...
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