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status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, epochs=500) model.predict(x=[15]) </s

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

# Create matrix another_matrix = tf.constant([[10. ,66.], [5. , 9.], [13. , 4.]], dtype=tf.float16) another_matrix <tf.Tensor: shape=(3, 2), dtype=float16, numpy= array([[10., 66.], [ 5., 9.], [13., 4.]], dtype=float16)> another_matrix.ndim 2

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

In General: To multiply an m×n matrix by an n×p matrix, the ns must be the same, and the result is an m×p matrix.