From 616f645e9498f1c0ccb078f4b66f5a86cc767993 Mon Sep 17 00:00:00 2001 From: Kaushal Jhawar Date: Sun, 27 Jan 2019 23:44:37 +0530 Subject: [PATCH] Hello MLCC... Assignment 4 problem solved. --- Kaushal2612.ipynb | 411 ++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 375 insertions(+), 36 deletions(-) diff --git a/Kaushal2612.ipynb b/Kaushal2612.ipynb index 0a721fb..3c9b61b 100644 --- a/Kaushal2612.ipynb +++ b/Kaushal2612.ipynb @@ -88,7 +88,11 @@ "metadata": { "id": "vmMcjzTxbWzw", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "28ef7d78-7772-412a-9350-e978ff41b4f3" }, "cell_type": "code", "source": [ @@ -97,8 +101,18 @@ "print (t2)\n", "print (t3)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Tensor(\"Const:0\", shape=(), dtype=float32)\n", + "Tensor(\"Const_1:0\", shape=(2,), dtype=float32)\n", + "Tensor(\"Const_2:0\", shape=(2, 3, 2), dtype=float32)\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -177,7 +191,11 @@ "metadata": { "id": "FyVz0GNqgreZ", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "3d79a3f0-ff25-45c9-b254-445a249e7712" }, "cell_type": "code", "source": [ @@ -193,8 +211,17 @@ "print ('Comp Graph 1 Alt: ', sess.run(comp_graph_1_alt))\n", "sess.close()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Comp Graph 1 : 7663\n", + "Comp Graph 1 Alt: 7663\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -212,7 +239,11 @@ "metadata": { "id": "4856BTvRhiBb", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "c6777128-c52f-442c-ab66-acd2da453f2b" }, "cell_type": "code", "source": [ @@ -231,8 +262,18 @@ "print ('Part 2 Result: ', part2_res)\n", "sess.close()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Complete Result: 3.5897436\n", + "Part 1 Result: -4.0\n", + "Part 2 Result: 3.5897436\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -257,18 +298,41 @@ "metadata": { "id": "-uHNe1BolJY0", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "d02a45de-a928-47cc-9de7-63dae71b6cda" }, "cell_type": "code", "source": [ "# Build the graph\n", "# YOUR CODE HERE\n", + "mul = tf.multiply(tf.constant([9.0, 10.0], dtype=tf.float32), tf.constant([7.0, 8.65], dtype=tf.float32))\n", + "graph_left = tf.divide(tf.cast( mul, dtype=tf.float32), tf.constant(5.6, dtype=tf.float32))\n", + "graph_right = tf.add( tf.constant([7.65, 9], dtype=tf.float32), tf.constant([13.5, 7.18], dtype=tf.float32))\n", + "comp = tf.minimum(graph_left, graph_right)\n", "\n", "# Execute \n", - "# YOUR CODE HERE" + "sess = tf.Session()\n", + "print ('Part 1 Result: ', sess.run(graph_left))\n", + "print ('Part 2 Result: ', sess.run(graph_right))\n", + "print ('Complete Result: ', sess.run(comp))\n", + "sess.close()\n", + "# YOUR CODE HERE\n" ], - "execution_count": 0, - "outputs": [] + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Part 1 Result: [11.25 15.446429]\n", + "Part 2 Result: [21.15 16.18]\n", + "Complete Result: [11.25 15.446429]\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -289,18 +353,49 @@ "metadata": { "id": "0ZhYwAlLmEvB", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + }, + "outputId": "5118a87b-08a8-4dd2-a93f-97441451d970" }, "cell_type": "code", "source": [ "# Build the graph\n", "# YOUR CODE HERE\n", - "\n", + "left_const1 = tf.constant([[1.2, 3.4], [7.5, 8.6]], dtype = tf.float32)\n", + "left_const2 = tf.constant([[7.0, 9.0], [8.0, 6.0]], dtype = tf.float32)\n", + "right_const1 = tf.constant([[2.79, 3.81, 5.6], [7.3, 5.67, 8.9]], dtype = tf.float32)\n", + "right_const2 = tf.constant([[2.6, 18.1], [7.86, 9.81], [9.36,10.91]], dtype = tf.float32)\n", + "\n", + "graph_left = tf.multiply(tf.cast(tf.reduce_mean(left_const1, axis = 1), dtype = tf.float32), left_const2)\n", + "transpose = tf.matrix_transpose(right_const2)\n", + "mul = tf.multiply(tf.cast( transpose, dtype = tf.float32 ), right_const1)\n", + "graph_right = tf.reduce_sum(mul)\n", + "comp = tf.math.add(graph_right,graph_left)\n", "# Execute \n", - "# YOUR CODE HERE" + "\n", + "# YOUR CODE HERE\n", + "sess = tf.Session()\n", + "print ('Part 1 Result: ', sess.run(graph_left))\n", + "print ('Part 2 Result: ', sess.run(graph_right))\n", + "print ('Complete Result: ', sess.run(comp))\n", + "sess.close()\n" ], - "execution_count": 0, - "outputs": [] + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Part 1 Result: [[16.100002 72.450005]\n", + " [18.400002 48.300003]]\n", + "Part 2 Result: 374.4683\n", + "Complete Result: [[390.5683 446.9183]\n", + " [392.8683 422.7683]]\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -318,18 +413,43 @@ "metadata": { "id": "GQWyCvsQmMcL", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + }, + "outputId": "17ffe109-f8d0-4ded-ecb3-86ffaf543c14" }, "cell_type": "code", "source": [ "# Build the graph\n", "# YOUR CODE HERE\n", + "const1 = tf.constant([[7.36, 8.91, 10.41], [5.31, 9.38, 7.99]] , dtype = tf.float32)\n", + "const2 = tf.constant([[7.99, 10.36], [5.36, 7.98], [8.91, 5.67]] , dtype = tf.float32)\n", + "const3 = tf.constant([[1.0, 5.6, 6.1, 8.0], [0, 0, 7.98, 9.0], [0, 0, 7.6, 7], [0, 0, 0, 8.98]] , dtype = tf.float32)\n", "\n", + "part1 = tf.add(tf.reduce_sum(const1 * tf.transpose(const2)), 7.0)\n", + "comp = tf.divide(tf.divide(part1,19.6),const3)\n", "# Execute \n", - "# YOUR CODE HERE" + "# YOUR CODE HERE\n", + "sess = tf.Session()\n", + "print ('Part 1 Result: ', sess.run(part1))\n", + "print ('Complete Result: ', sess.run(comp))\n", + "sess.close()\n" ], - "execution_count": 0, - "outputs": [] + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Part 1 Result: 381.48438\n", + "Complete Result: [[19.463488 3.475623 3.1907358 2.432936 ]\n", + " [ inf inf 2.4390335 2.1626098]\n", + " [ inf inf 2.5609853 2.7804983]\n", + " [ inf inf inf 2.1674263]]\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -388,7 +508,11 @@ "metadata": { "id": "1h1-D8K1uT48", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "9c81393c-7739-404f-90c0-217aa01db5f8" }, "cell_type": "code", "source": [ @@ -396,8 +520,21 @@ "plt.plot(train_X[:10], train_Y[:10], 'g')\n", "plt.show()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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liagY+QX5+DPzTuEh7cK95vSbyMh78c+hg6UjOtZ1R1O7ZnCWP99jbmLnjHo1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\nku1WhaqcY3XFGYqDcxQH5yg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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { @@ -490,7 +627,11 @@ "metadata": { "id": "ttI7ZT-ozAm1", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 721 + }, + "outputId": "3e39fc94-0452-4664-cf2a-c4a48469e80e" }, "cell_type": "code", "source": [ @@ -524,8 +665,49 @@ " plt.legend()\n", " plt.show()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48249.703\n", + "Loss after epoch 50 is 30.226511\n", + "Loss after epoch 100 is 30.215973\n", + "Loss after epoch 150 is 30.20563\n", + "Loss after epoch 200 is 30.195286\n", + "Loss after epoch 250 is 30.18489\n", + "Loss after epoch 300 is 30.174564\n", + "Loss after epoch 350 is 30.16422\n", + "Loss after epoch 400 is 30.15389\n", + "Loss after epoch 450 is 30.143536\n", + "Loss after epoch 500 is 30.133192\n", + "Loss after epoch 550 is 30.122887\n", + "Loss after epoch 600 is 30.11254\n", + "Loss after epoch 650 is 30.102207\n", + "Loss after epoch 700 is 30.091913\n", + "Loss after epoch 750 is 30.081572\n", + "Loss after epoch 800 is 30.071259\n", + "Loss after epoch 850 is 30.060925\n", + "Loss after epoch 900 is 30.050634\n", + "Loss after epoch 950 is 30.040358\n", + "Now testing the model in the test set\n", + "The final loss is: 35.75786\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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k+ZcUshCFgNVppfe6nmxM+p4Hwx5mabvPCfaTY3rizmkvnMc0dSJ+y5agcbtx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\n+jVMH0zHOHcWGpsNV8VKWCLHkNH+KZl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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { @@ -551,7 +733,35 @@ "source": [ "def linear_regression(learning_rate=0.000005, n_epochs=100, interval=50):\n", " # YOUR CODE HERE\n", - " pass" + " x = tf.placeholder(tf.float32, name='x')\n", + " y = tf.placeholder(tf.float32, name='y')\n", + " W = tf.Variable(np.random.random_sample(), name='weight_1')\n", + " b = tf.Variable(np.random.random_sample(), name='bias_1')\n", + "\n", + " pred_y = (W*x) + b \n", + " loss = tf.reduce_mean(tf.square(y - pred_y))\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)\n", + " with tf.Session() as sess:\n", + " # We need to initialize the variables in our graph\n", + " sess.run(tf.global_variables_initializer())\n", + "\n", + " for epoch in range(n_epochs):\n", + " _, curr_loss = sess.run([optimizer, loss], feed_dict={x:train_X, y:train_Y})\n", + "\n", + " if epoch % interval == 0:\n", + " print ('Loss after epoch', epoch, ' is ', curr_loss)\n", + "\n", + " print ('Now testing the model in the test set')\n", + " final_preds, final_loss = sess.run([pred_y, loss], feed_dict={x:test_X, y:test_Y})\n", + "\n", + "\n", + " print ('The final loss is: ', final_loss)\n", + "\n", + " # Plotting the final predictions against the true predictions\n", + " plt.plot(test_X, test_Y, 'g', label='Actual Function')\n", + " plt.plot(test_X, final_preds, 'r', label='Predicted Function')\n", + " plt.legend()\n", + " plt.show()\n" ], "execution_count": 0, "outputs": [] @@ -560,28 +770,108 @@ "metadata": { "id": "A6MaclhK4rc6", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + }, + "outputId": "cc8b52cd-6801-4ac4-9722-d1367d31b391" }, "cell_type": "code", "source": [ "# Okay! Now let's tweak!\n", "linear_regression(learning_rate=0.000034, n_epochs=500)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 27904.5\n", + "Loss after epoch 50 is 35.159245\n", + "Loss after epoch 100 is 35.07696\n", + "Loss after epoch 150 is 34.994877\n", + "Loss after epoch 200 is 34.91298\n", + "Loss after epoch 250 is 34.831253\n", + "Loss after epoch 300 is 34.749763\n", + "Loss after epoch 350 is 34.66843\n", + "Loss after epoch 400 is 34.587337\n", + "Loss after epoch 450 is 34.506393\n", + "Now testing the model in the test set\n", + "The final loss is: 40.97552\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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v0BnfYh6FchBEQSeBLIQQNpKc/ogxR4azLeJbPJw8+az5et6p8J7N66oePkA7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\nioLztq3ogiegjo8jq1Ig+tAwMuu/bvv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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { "id": "peoHmV2M40uU", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 721 + }, + "outputId": "75ce2926-cbce-40a7-feda-667f79c3710c" }, "cell_type": "code", "source": [ "linear_regression(learning_rate=0.0000006, n_epochs=1000)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 33290.74\n", + "Loss after epoch 50 is 3834.4224\n", + "Loss after epoch 100 is 468.1936\n", + "Loss after epoch 150 is 83.504684\n", + "Loss after epoch 200 is 39.541904\n", + "Loss after epoch 250 is 34.516758\n", + "Loss after epoch 300 is 33.941273\n", + "Loss after epoch 350 is 33.87425\n", + "Loss after epoch 400 is 33.86536\n", + "Loss after epoch 450 is 33.86309\n", + "Loss after epoch 500 is 33.861607\n", + "Loss after epoch 550 is 33.860188\n", + "Loss after epoch 600 is 33.8588\n", + "Loss after epoch 650 is 33.857353\n", + "Loss after epoch 700 is 33.85598\n", + "Loss after epoch 750 is 33.854576\n", + "Loss after epoch 800 is 33.85319\n", + "Loss after epoch 850 is 33.851734\n", + "Loss after epoch 900 is 33.85036\n", + "Loss after epoch 950 is 33.848957\n", + "Now testing the model in the test set\n", + "The final loss is: 40.295074\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ouG/ehH7qRNTx8VgqV8UQtgDLi3XwzOeVJzeCyO0kkIUQwkGM6UYm/BjMZxc2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\nTye1dTuMs+dhK1L0ya8VQohMkkAWOVq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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { @@ -597,11 +887,60 @@ "metadata": { "id": "JKiHjGN15HPX", "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + }, + "outputId": "5c984269-0e34-4588-9f6f-f4b64c193d6b" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "linear_regression(learning_rate=0.000034, n_epochs=10000, interval = 1000)" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 32524.732\n", + "Loss after epoch 1000 is 29.348707\n", + "Loss after epoch 2000 is 28.013582\n", + "Loss after epoch 3000 is 26.74133\n", + "Loss after epoch 4000 is 25.528984\n", + "Loss after epoch 5000 is 24.373766\n", + "Loss after epoch 6000 is 23.272974\n", + "Loss after epoch 7000 is 22.22403\n", + "Loss after epoch 8000 is 21.224514\n", + "Loss after epoch 9000 is 20.27205\n", + "Now testing the model in the test set\n", + "The final loss is: 23.085957\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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oMfn9M//ABsPPP2Ia1A/9mdO4nyiEdUoUjuYtVOhYPGwSyEIIkYXcHjczD00n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\nQf3QnzuLu3ARrFOicDRtnv6GQgiRQRL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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "kn5QW_kPcX-P", + "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ - "# YOUR CODE HERE" + "" ], "execution_count": 0, "outputs": []