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for _ in range(100):
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# Without `clear_session()`, each iteration of this loop will
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# slightly increase the size of the global state managed by Keras
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model = tf.keras.Sequential([tf.keras.layers.Dense(10) for _ in range(10)])
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for _ in range(100):
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# With `clear_session()` called at the beginning,
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# Keras starts with a blank state at each iteration
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# and memory consumption is constant over time.
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tf.keras.backend.clear_session()
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model = tf.keras.Sequential([tf.keras.layers.Dense(10) for _ in range(10)])
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Example 2: resetting the layer name generation counter
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>>> import tensorflow as tf
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>>> layers = [tf.keras.layers.Dense(10) for _ in range(10)]
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>>> new_layer = tf.keras.layers.Dense(10)
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>>> print(new_layer.name)
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dense_10
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>>> tf.keras.backend.set_learning_phase(1)
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>>> print(tf.keras.backend.learning_phase())
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1
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>>> tf.keras.backend.clear_session()
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>>> new_layer = tf.keras.layers.Dense(10)
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>>> print(new_layer.name)
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dense
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floatx function
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tf.keras.backend.floatx()
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Returns the default float type, as a string.
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E.g. 'float16', 'float32', 'float64'.
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Returns
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String, the current default float type.
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Example
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>>> tf.keras.backend.floatx()
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'float32'
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set_floatx function
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tf.keras.backend.set_floatx(value)
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Sets the default float type.
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Note: It is not recommended to set this to float16 for training, as this will likely cause numeric stability issues. Instead, mixed precision, which is using a mix of float16 and float32, can be used by calling tf.keras.mixed_precision.experimental.set_policy('mixed_float16'). See the mixed precision guide for details.
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Arguments
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value: String; 'float16', 'float32', or 'float64'.
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Example
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>>> tf.keras.backend.floatx()
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'float32'
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>>> tf.keras.backend.set_floatx('float64')
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>>> tf.keras.backend.floatx()
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'float64'
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>>> tf.keras.backend.set_floatx('float32')
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Raises
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ValueError: In case of invalid value.
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image_data_format function
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tf.keras.backend.image_data_format()
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Returns the default image data format convention.
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Returns
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A string, either 'channels_first' or 'channels_last'
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Example
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>>> tf.keras.backend.image_data_format()
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'channels_last'
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set_image_data_format function
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tf.keras.backend.set_image_data_format(data_format)
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Sets the value of the image data format convention.
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Arguments
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data_format: string. 'channels_first' or 'channels_last'.
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Example
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>>> tf.keras.backend.image_data_format()
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'channels_last'
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>>> tf.keras.backend.set_image_data_format('channels_first')
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>>> tf.keras.backend.image_data_format()
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'channels_first'
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>>> tf.keras.backend.set_image_data_format('channels_last')
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Raises
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ValueError: In case of invalid data_format value.
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epsilon function
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