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rakesh kumar
rakesh kumar

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list out the checklist of Tensor Model Building Operations in tensor flow

Sequential Model Creation - tf.keras.Sequential:

Example:

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(10, activation='softmax')
])
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Output:
Creating a sequential model with two dense layers for a simple feedforward neural network.
Functional API Model Building:

Example:

inputs = tf.keras.layers.Input(shape=(784,))
x = tf.keras.layers.Dense(64, activation='relu')(inputs)
outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
model = tf.keras.Model(inputs, outputs)
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Output:
Building a model using the functional API for greater flexibility.
Custom Layer Creation - Subclassing tf.keras.layers.Layer:

Example:

class MyDenseLayer(tf.keras.layers.Layer):
    def __init__(self, units):
        super(MyDenseLayer, self).__init__()
        self.units = units

    def build(self, input_shape):
        self.w = self.add_weight("weights", shape=(input_shape[-1], self.units))
        self.b = self.add_weight("bias", shape=(self.units,))

    def call(self, inputs):
        return tf.matmul(inputs, self.w) + self.b

my_layer = MyDenseLayer(64)
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Output:
Creating a custom layer by subclassing tf.keras.layers.Layer.
Model Compilation - model.compile:

Example:

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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Output:
Compiling the model with an optimizer, loss function, and evaluation metrics.
Model Summary - model.summary():

Example:

model.summary()
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Output:
Displaying a summary of the model architecture with layer names, output shapes, and parameter counts.
Model Visualization - tf.keras.utils.plot_model:

Example:

tf.keras.utils.plot_model(model, to_file='model.png', show_shapes=True)
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Output:
Generating a graphical visualization of the model architecture.
Model Loading - tf.keras.models.load_model:

Example:

loaded_model = tf.keras.models.load_model('saved_model.h5')
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Output:
Loading a pre-trained model from a saved model file.
Model Training - model.fit:

Example:

history = model.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))
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Output:
Training the model using provided data and monitoring training progress and validation metrics.
Fine-Tuning Pretrained Models - Transfer Learning:

Example:

base_model = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=False)
base_model.trainable = False  # Freeze base model layers
inputs = tf.keras.layers.Input(shape=(224, 224, 3))
x = base_model(inputs)
outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
model = tf.keras.Model(inputs, outputs)
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Output:
Building a new model by fine-tuning a pretrained MobileNetV2 model for a specific task.

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