python搭建迴圈神經網路__python

吳恩達深度學習第五課編程題一

匯入模組

import numpy as npfrom rnn_utils import *

迴圈神經網路小單元向前傳播

# GRADED FUNCTION: rnn_cell_forwarddef rnn_cell_forward(xt, a_prev, parameters):    """    Implements a single forward step of the RNN-cell as described in Figure (2)    Arguments:    xt -- your input data at timestep "t", numpy array of shape (n_x, m).    a_prev -- Hidden state at timestep "t-1", numpy array of shape (n_a, m)    parameters -- python dictionary containing:                        Wax -- Weight matrix multiplying the input, numpy array of shape (n_a, n_x)                        Waa -- Weight matrix multiplying the hidden state, numpy array of shape (n_a, n_a)                        Wya -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)                        ba --  Bias, numpy array of shape (n_a, 1)                        by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)    Returns:    a_next -- next hidden state, of shape (n_a, m)    yt_pred -- prediction at timestep "t", numpy array of shape (n_y, m)    cache -- tuple of values needed for the backward pass, contains (a_next, a_prev, xt, parameters)    """        # Retrieve parameters from "parameters"    Wax = parameters["Wax"]    Waa = parameters["Waa"]    Wya = parameters["Wya"]    ba = parameters["ba"]    by = parameters["by"]        ### START CODE HERE ### (≈2 lines)    # compute next activation state using the formula given above    a_next = np.tanh(np.dot(Wax,xt)+np.dot(Waa,a_prev)+ba)    # compute output of the current cell using the formula given above    yt_pred = softmax(np.dot(Wya,a_next)+by)       ### END CODE HERE ###        # store values you need for backward propagation in cache    cache = (a_next, a_prev, xt, parameters)        return a_next, yt_pred, cache

測試向前傳播

np.random.seed(1)xt = np.random.randn(3,10)a_prev = np.random.randn(5,10)Waa = np.random.randn(5,5)Wax = np.random.randn(5,3)Wya = np.random.randn(2,5)ba = np.random.randn(5,1)by = np.random.randn(2,1)parameters = {"Waa": Waa, "Wax": Wax, "Wya": Wya, "ba": ba, "by": by}a_next, yt_pred, cache = rnn_cell_forward(xt, a_prev, parameters)print("a_next[4] = ", a_next[4])print("a_next.shape = ", a_next.shape)print("yt_pred[1] =", yt_pred[1])print("yt_pred.shape = ", yt_pred.shape)

總時間步長的向前傳播

# GRADED FUNCTION: rnn_forwarddef rnn_forward(x, a0, parameters):    """    Implement the forward propagation of the recurrent neural network described in Figure (3).    Arguments:    x -- Input data for every time-step, of shape (n_x, m, T_x).    a0 -- Initial hidden state, of shape (n_a, m)    parameters -- python dictionary containing:                        Waa -- Weight matrix multiplying the hidden state, numpy array of shape (n_a, n_a)                        Wax -- Weight matrix multiplying the input, numpy array of shape (n_a, n_x)                        Wya -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)                        ba --  Bias numpy array of shape (n_a, 1)                        by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)    Returns:    a -- Hidden states for every time-step, numpy array of shape (n_a, m, T_x)    y_pred -- Predictions for every time-step, numpy array of shape (n_y, m, T_x)    caches -- tuple of values needed for the backward pass, contains (list of caches, x)    """        # Initialize "caches" which will contain the list of all caches    caches = []        # Retrieve dimensions from shapes of x and parameters["Wya"]    n_x, m, T_x = x.shape    n_y, n_a = parameters["Wya"].shape        ### START CODE HERE ###        # initialize "a" and "y" with zeros (≈2 lines)    a = np.zeros((n_a, m, T_x))    y_pred = np.zeros((n_y, m, T_x))        # Initialize a_next (≈1 line)    a_next = a0        # loop over all time-steps    for t in range(T_x):        # Update next hidden state, compute the prediction, get the cache (≈1 line)        a_next, yt_pred, cache = rnn_cell_forward(xt, a_prev, parameters)        # Save the value of the new "next" hidden state in a (≈1 line)        a[:,:,t] = a_next        # Save the value of the prediction in y (≈1 line)        y_pred[:,:,t] = yt_pred        # Append "cache" to "caches" (≈1 line)        caches.append(cache)            ### END CODE HERE ###        # store values needed for backward propagation in cache    caches = (caches, x)        return a, y_pred, caches

測試向前傳播計算結果

np.random.seed(1)x = np.random.randn(3,10,4)a0 = np.random.randn(5,10)Waa = np.random.randn(5,5)Wax = np.random.randn(5,3)Wya = np.random.randn(2,5)ba = np.random.randn(5,1)by = np.random.randn(2,1)parameters = {"Waa": Waa, "Wax": Wax, "Wya": Wya, "ba": ba, "by": by}a, y_pred, caches = rnn_forward(x, a0, parameters)print("a[4][1] = ", a[4][1])print("a.shape = ", a.shape)print("y_pred[1][3] =", y_pred[1][3])print("y_pred.shape = ", y_pred.shape)print("caches[1][1][3] =", caches[1][1][3])print("len(caches) = ", len(caches))

未完待續