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Fix numpy David model training #39

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Fix numpy David model training #39

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10 changes: 5 additions & 5 deletions 01_mysteries_of_neural_networks/03_numpy_neural_net/Numpy deep neural network.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -191,8 +191,8 @@
"\n",
"def relu_backward(dA, Z):\n",
" dZ = np.array(dA, copy = True)\n",
" dZ[Z <= 0] = 0;\n",
" return dZ;"
" dZ[Z <= 0] = 0\n",
" return dZ"
]
},
{
Expand Down Expand Up @@ -451,7 +451,7 @@
" Y = Y.reshape(Y_hat.shape)\n",
" \n",
" # initiation of gradient descent algorithm\n",
" dA_prev = - (np.divide(Y, Y_hat) - np.divide(1 - Y, 1 - Y_hat));\n",
" dA_prev = - (np.divide(Y, Y_hat) - np.divide(1 - Y, 1 - Y_hat))\n",
" \n",
" for layer_idx_prev, layer in reversed(list(enumerate(nn_architecture))):\n",
" # we number network layers from 1\n",
Expand Down Expand Up @@ -503,7 +503,7 @@
" params_values[\"W\" + str(layer_idx)] -= learning_rate * grads_values[\"dW\" + str(layer_idx)] \n",
" params_values[\"b\" + str(layer_idx)] -= learning_rate * grads_values[\"db\" + str(layer_idx)]\n",
"\n",
" return params_values;"
" return params_values"
]
},
{
Expand Down Expand Up @@ -750,7 +750,7 @@
"outputs": [],
"source": [
"# Training\n",
"params_values = train(np.transpose(X_train), np.transpose(y_train.reshape((y_train.shape[0], 1))), NN_ARCHITECTURE, 10000, 0.01)[0]"
"params_values = train(np.transpose(X_train), np.transpose(y_train.reshape((y_train.shape[0], 1))), NN_ARCHITECTURE, 10000, 0.01)"
]
},
{
Expand Down