From b40de960f950cb90d85bbd7e7bee9c4a632f91c8 Mon Sep 17 00:00:00 2001 From: Jan Date: Sun, 3 Jul 2022 00:42:01 -0600 Subject: [PATCH] last term ouf output sequence the same in hand calc and rnn next return sequences --- fmda_kf_rnn.ipynb | 34 +++++++++++++++++++++++++--------- fmda_kf_rnn.ipynb.pdf | Bin 3507053 -> 3259725 bytes 2 files changed, 25 insertions(+), 9 deletions(-) diff --git a/fmda_kf_rnn.ipynb b/fmda_kf_rnn.ipynb index c9ed2c5..61e6075 100644 --- a/fmda_kf_rnn.ipynb +++ b/fmda_kf_rnn.ipynb @@ -1442,6 +1442,14 @@ " model = tf.keras.Model(inputs=inputs, outputs=x)\n", " model.compile(loss='mean_squared_error', optimizer='adam')\n", " return model\n", + "def create_RNN_functional(hidden_units, dense_units, input_shape, activation):\n", + " inputs = tf.keras.Input(shape=input_shape)\n", + " x = tf.keras.layers.SimpleRNN(hidden_units, input_shape=input_shape,\n", + " activation=activation[0])(inputs)\n", + " outputs = tf.keras.layers.Dense(dense_units, activation=activation[1])(x)\n", + " model = tf.keras.Model(inputs=inputs, outputs=outputs)\n", + " model.compile(loss='mean_squared_error', optimizer='adam')\n", + " return model\n", "def create_fit_predict_RNN(hidden_units, dense_units, \n", " samples, timesteps, features, dense_layers=1, activation=['tanh', 'tanh']):\n", " # statefull model version with with fixed number of batches\n", @@ -1466,6 +1474,7 @@ "dense_units=1\n", "dense_layers=1\n", "features=1\n", + "hours=Et.shape[0]\n", "# statefull model version for traning\n", "model_fit=create_RNN_2(hidden_units=hidden_units, dense_units=dense_units, \n", " batch_shape=(samples, timesteps, features),stateful = True,\n", @@ -1475,7 +1484,13 @@ "# and start from the state there\n", "model_predict=create_RNN_2(hidden_units=hidden_units, dense_units=dense_units, \n", " input_shape=(hours,features),stateful = False,\n", - " activation=activation,dense_layers=dense_layers) \n", + " activation=activation,dense_layers=dense_layers)\n", + "model_predict=create_RNN_functional(hidden_units=1, dense_units=1, input_shape=(hours,1), \n", + " activation=['linear', 'linear'])\n", + "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", "# not fitting yet\n", "# fmda_model.fit(x_train, y_train, epochs=40, verbose=2,batch_size=samples)" ], @@ -1492,20 +1507,18 @@ "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", - "hours=Et.shape[0]\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", - " out1[i]=np.dot(h,w1[3]) + w1[4]\n", - "# print('out=',out)\n", + " out=np.dot(h,w1[3]) + w1[4]\n", + " out1[i,0]=out\n", + "print('out=',out)\n", "model_predict.set_weights(w1)\n", "# prediction on the entire dataset from zero state\n", - "mt = model_predict.predict(Et)\n", - "# print('mt=',mt)\n", - "if scale:\n", - " mt = scalery.inverse_transform(mt)\n", - "m = mt[:,0]" + "x_input=np.reshape(Et,(1, hours, 1))\n", + "m = model_predict.predict(x_input)\n", + "print('m=',m)" ], "metadata": { "id": "PZw7DNQD4Inr" @@ -1528,6 +1541,9 @@ { "cell_type": "code", "source": [ + "if scale:\n", + " mt = scalery.inverse_transform(mt)\n", + "m = mt[:,0]\n", "plot_m(m,title='Actual RNN prediction same weights')" ], "metadata": { diff --git a/fmda_kf_rnn.ipynb.pdf b/fmda_kf_rnn.ipynb.pdf index f7e5d959e1a6df0c91d469319b16342ec36f052b..99c036717b78144ac2d1ccdabadf6b7d16f5633f 100644 GIT binary patch literal 3259725 zcwX$ibzEJ~wKk3fNFZ2n3kL|U2X}V~?k)#+3r=wN1b24{?!n!IJHZ`->yPwpZ`=EA zZ`%Ia`}Vz;#r|xbS$p=ZH8X#3&RWkJh-CzYsev?%u*4I4JFBoj03EQNY;N$^t|Sq@$;y 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