Created
May 27, 2019 11:59
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| def mean_squared_error( Y , y_pred ): | |
| return tf.reduce_mean( tf.square( y_pred - Y ) ) | |
| def mean_squared_error_deriv( Y , y_pred ): | |
| return tf.reshape( tf.reduce_mean( 2 * ( y_pred - Y ) ) , [ 1 , 1 ] ) | |
| def h ( X , weights , bias ): | |
| return tf.tensordot( X , weights , axes=1 ) + bias |
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