From 8d6e1a2fbb7c930eb989f8f8d2a11d0a7445a80a Mon Sep 17 00:00:00 2001 From: Jan Date: Sun, 3 Jul 2022 21:22:43 -0600 Subject: [PATCH] cleaned up exact/initial/fitted weights --- fmda_kf_rnn.ipynb | 129 +++++++++++++++++++++++++++++--------------------- fmda_kf_rnn.ipynb.pdf | Bin 3786118 -> 3062035 bytes 2 files changed, 75 insertions(+), 54 deletions(-) diff --git a/fmda_kf_rnn.ipynb b/fmda_kf_rnn.ipynb index 8dd1c57..96cbc13 100644 --- a/fmda_kf_rnn.ipynb +++ b/fmda_kf_rnn.ipynb @@ -39,6 +39,15 @@ }, { "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "Snyr0_3sGxty" + } + }, + { + "cell_type": "markdown", "metadata": { "id": "WHIgN2uZ689b" }, @@ -675,7 +684,7 @@ " plt.xlabel('Time (hours)')\n", " plt.ylabel('Fuel moisture content (%)')\n", " plt.legend()\n", - " return E,m_f,data,hour,h2\n" + " return E,m_f,data,hour,h2,DeltaE\n" ] }, { @@ -686,7 +695,7 @@ }, "outputs": [], "source": [ - "E,m_f,data,hour,h2 = create_synthetic_data(days=20,power=4,data_noise=0.000,process_noise=0.0,DeltaE=0.0) " + "E,m_f,data,hour,h2,DeltaE = create_synthetic_data(days=20,power=4,data_noise=0.000,process_noise=0.0,DeltaE=0.0) " ] }, { @@ -1385,7 +1394,7 @@ { "cell_type": "code", "source": [ - "E,m_f,data,hour,h2 = create_synthetic_data(days=20,power=4,data_noise=0.00,process_noise=0.0,DeltaE=0.0) " + "E,m_f,data,hour,h2,DeltaE = create_synthetic_data(days=20,power=4,data_noise=0.00,process_noise=0.0,DeltaE=0.1) " ], "metadata": { "id": "rHWxqARpSO_f" @@ -1494,11 +1503,7 @@ "print(model_predict.summary())\n", "from keras.utils.vis_utils import plot_model\n", "plot_model(model_predict, to_file='model_plot.png', \n", - " show_shapes=True, show_layer_names=True)\n", - "# fitting\n", - "w1=[np.array([[1.-np.exp(-0.1)]]), np.array([[np.exp(-0.1)]]), np.array([0.]),np.array([[1.0]]),np.array([0.0])]\n", - "model_fit.set_weights(w1)\n", - "model_fit.fit(x_train, y_train, epochs=40, verbose=2,batch_size=samples)" + " show_shapes=True, show_layer_names=True)" ], "metadata": { "id": "ZjJxHiEVL5sJ" @@ -1509,27 +1514,16 @@ { "cell_type": "code", "source": [ - "w=model_fit.get_weights()\n", - "w1=[np.array([[1.-np.exp(-0.1)]]), np.array([[np.exp(-0.1)]]), np.array([0.]),np.array([[1.0]]),np.array([0.0])]\n", - "for i in range(len(w)):\n", - " print(i,'w:',w[i],w[i].shape,' w1:',w1[i],w1[i].shape,)\n", - "h=0\n", - "out1 = np.empty((hours,1))\n", - "for i in range(Et.shape[0]):\n", - " h =np.dot(Et[i,0],w1[0])+np.dot(h,w1[1]) + w1[2]\n", - " out=np.dot(h,w1[3]) + w1[4]\n", - " out1[i,0]=out\n", - "print('out1.shape=',out1.shape)\n", - "model_predict.set_weights(w1)\n", - "# prediction on the entire dataset from zero state\n", - "x_input=np.reshape(Et,(1, hours, 1))\n", - "m = model_predict.predict(x_input)\n", - "print('m.shape=',m.shape)\n", - "m = np.reshape(m,hours)\n", - "print('m.shape=',m.shape)" + "# fitting\n", + "w_exact=[np.array([[1.-np.exp(-0.1)]]), np.array([[np.exp(-0.1)]]), np.array([0.]),np.array([[1.0]]),np.array([-DeltaE])]\n", + "w_initial=[np.array([[1.-np.exp(-0.1)]]), np.array([[np.exp(-0.1)]]), np.array([0.]),np.array([[1.0]]),np.array([0.0])]\n", + "w_initial=[np.array([[1.-np.exp(-0.1)]]), np.array([[np.exp(-0.1)]]), np.array([0.]),np.array([[1.0]]),np.array([-0.5*DeltaE])]\n", + "model_fit.set_weights(w_initial)\n", + "model_fit.fit(x_train, y_train, epochs=10000, verbose=0,batch_size=samples)\n", + "w_fitted=model_fit.get_weights()" ], "metadata": { - "id": "PZw7DNQD4Inr" + "id": "dE0OHg0tGVE9" }, "execution_count": null, "outputs": [] @@ -1537,11 +1531,11 @@ { "cell_type": "code", "source": [ - "print(out1.shape)\n", - "plot_m(out1,title='Hand computed RNN prediction')" + "for i in range(len(w)):\n", + " print('weight',i,' exact:',w_exact[i],': initial:',w_initial[i],' fitted:',w_fitted[i])" ], "metadata": { - "id": "bO1ewTj9gGvg" + "id": "H2Fi9eoiHhdd" }, "execution_count": null, "outputs": [] @@ -1549,54 +1543,79 @@ { "cell_type": "code", "source": [ + "out = np.empty((hours,1))\n", + "w=w_exact\n", + "h=0\n", + "for i in range(Et.shape[0]):\n", + " h=np.dot(Et[i,0],w[0])+np.dot(h,w[1]) + w[2]\n", + " out[i]=np.dot(h,w[3]) + w[4]\n", "if scale:\n", - " m = scalery.inverse_transform(m)\n", - "print('m.shape=',m.shape)\n", - "plot_m(m,title='Actual RNN prediction same weights')" + " print('scaling')\n", + " out = scalery.inverse_transform(out)\n", + "out=np.reshape(out,hours)\n", + "plot_m(out,title='Hand computed RNN prediction with exact weights')" ], "metadata": { - "id": "henChC0cmbuy" + "id": "PZw7DNQD4Inr" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "def model_eval(w,title):\n", + " # prediction on the entire dataset from zero state\n", + " model_predict.set_weights(w)\n", + " x_input=np.reshape(Et,(1, hours, 1))\n", + " y_output = model_predict.predict(x_input)\n", + " m = np.reshape(y_output,hours)\n", + " print('weights=',w)\n", + " if scale:\n", + " print('scaling')\n", + " m = scalery.inverse_transform(m)\n", + " m = np.reshape(m,hours)\n", + " plot_m(m,title=title)\n", + " return m" + ], "metadata": { - "id": "R2jkoZlAIaSb" + "id": "-T8lCS6nBHCj" }, - "outputs": [], - "source": [ - "" - ] + "execution_count": null, + "outputs": [] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "m_exact=model_eval(w_exact,'RNN prediction with exact weights')\n", + "print('max abs diff',np.max(np.abs(m_exact-out)))" + ], "metadata": { - "id": "NCZ_imOaUH4l" + "id": "bO1ewTj9gGvg" }, - "outputs": [], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", "source": [ - "x = tf.zeros([3,4])\n", - "x.shape[0]\n", - "trainsteps,outputs = x.shape\n", - "trainsteps,outputs\n", - "y = tf.zeros([trainsteps,features])\n", - "print(x[1,:])\n", - "print(x)\n" - ] + "m_initial=model_eval(w_initial,'RNN prediction with initial weights')" + ], + "metadata": { + "id": "henChC0cmbuy" + }, + "execution_count": null, + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "0Oz3kVLljDwt" + "id": "R2jkoZlAIaSb" }, "outputs": [], "source": [ - "tf.zeros([1,2,3])" + "m_fitted=model_eval(w_fitted,'RNN prediction with fitted weights')" ] }, { @@ -2624,7 +2643,9 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" - } + }, + "accelerator": "GPU", + "gpuClass": "standard" }, "nbformat": 4, "nbformat_minor": 0 diff --git a/fmda_kf_rnn.ipynb.pdf b/fmda_kf_rnn.ipynb.pdf index 80dddbf4827c64ce44b6af31cbd733691c28261a..7eec24106402a555c15bef02c707370c3ebf5d8f 100644 GIT binary patch literal 3062035 zcwX$fRd8L+lIJUCX4V!nY%w!4T5K^hGg-_mS19DnDY?$bSeW2PVO z!<~t*c15kItjw%`)qctKi$$R%Ccyw?WP_tvI6vKk0|J-<_C{84e0)rzPNs$~_D%q5 zd3!HQ8yiC=c1C7^F15U&v8A1hy|V=`K-$j5)CTa!08mr~Xaa!D`apJlPF)&+u!Dn* zsfMYMtfdPRJ1Zw6E61NeSt&JnIeLJNrL`$Q($v`6{*R-mg_FIlDHD*Hg^`(&otc@P z5eQ@ls2Z9XI$8c(q@t1v6BpN?bv8C23nM!#4IGn_lf8+nvFRUes@9fF(c 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