{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Universal Approximation - an dynamic point of view" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We shall in the sequel take an unusual (but familiar to many researchers) point of view towards deep neural networks. Consider a controlled ordinary differential equation (ODE), i.e.\n", "$$\n", "(\\ast) \\qquad dX_t = \\sum_{i=1}^d V_i(X_t) du^i(t), \\; X_0 = x\n", "$$\n", "where $ V_i $ are some smooth vector fields on $ \\mathbb{R}^m $ with all proper derivatives bounded. $ u $ is a finite variation curve with values in $ \\mathbb{R}^d $. For simplicity we shall always consider finitely many jumps of $ u $ in this lecture, and $ u $ being $ C^\\infty$ between two jumps.\n", "\n", "A solution of this equation is given by a fixed point of\n", "$$\n", "X_t = x + \\sum_{i=1}^d \\int_0^t V_i(X_s) du^i(s)\n", "$$\n", "for $ t \\geq 0 $. Notice that the integral is well defined due to the finite variation property.\n", "\n", "We shall analyze two aspects in the sequel:\n", "1. Connection to deep neural networks.\n", "2. How expressive are those networks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the first aspect consider vector fields of the form\n", "$$\n", "V(x) = \\sum_{j=1}^N \\alpha_j \\phi(\\langle \\mu_j , x \\rangle + \\beta_j)\n", "$$\n", "for some vectors $ \\alpha_j, \\mu_j \\in \\mathbb{R}^m $ and numbers $ \\beta_j $. Notice that we could also take vector fields of the form\n", "$$\n", "V(x) = W {\\phi}(x) + a - x\n", "$$\n", "for matrices $ W $ and vectors $ a $ to achive the same result.\n", "\n", "In this case the Euler scheme, which approximates the solution of the ODE, is an iterative procedure where maps of the type\n", "$$\n", "x \\mapsto x + \\sum_{i=1}^d V_i(x) \\Delta u^i(t)\n", "$$\n", "at point $ x \\in \\mathbb{R}^N $ and $ t \\geq 0 $ are concatenated. Obviously one can understand such finite concatenations as deep neural networks with at most $ m + N $ neurons in each layers and depths being equal to the time discretization with respect to the ODE's initial value.\n", "\n", ">We therefore call the map $ x \\mapsto X_t $ a deep neural network of infinite depths. Notice that for piecewise constant $ u $ we just obtain classical neural networks of finite depths." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that one could also consider the Picard-Lindelof iteration scheme to obtain neural networks, then depth would be related to the iteration step.\n", "\n", "This obvious point of view has been taken in several papers, most recently in [Neural ordinary differential equations](https://arxiv.org/pdf/1806.07366.pdf). There are three new aspects in our approach: first the control approach, i.e. the use of additional control variables $u^i$, second, that actually every feedforward neural network is this very form, and, third, the obvious question how the choice of the vector fields influences the expressibility of the networks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the third aspect consider a training data set $ {(x_l,y_l)}_{l \\in L} $, i.e. a subset of a graph of an $ \\mathbb{R}^m$-valued continuous function on some compact set. The cardinality of $L$ should be seen as large but finite.\n", "\n", "Consider the following ODE on $ \\mathbb{R}^{mL} $\n", "$$\n", "dZ_t = \\sum_{i=1}^d W_i(Z_t) du^i(t) \\, ,\n", "$$\n", "where\n", "$$\n", "(W_i((x_l)))_l:=V_i(x_l)\n", "$$\n", "for $ l \\in L $ and $ i = 1,\\ldots,d $. We actually make a _particle system_ of $ L $ particles moving precisely according to the dynamics of $ (\\ast) $. This system also has a unique solution for all times just as equation $(\\ast)$.\n", "\n", "Representability of the non-linear function on the training set amounts precisely to saying that, for given $ t > 0 $, the particle system $ Z $ can possibly reach at $ t $ any point $ (y_l) $ if starting at $ (x_l) $ for some choice of control $u$.\n", "\n", "For such questions the notion of controllability has been introduced, in particular [Chow's theorem](https://en.wikipedia.org/wiki/Chow%E2%80%93Rashevskii_theorem):\n", "\n", "\n", "If the Lie brackets of $ (W_i) $ generate at the point $ (x_l) $ the whole space $ \\mathbb{R}^{mL} $, then any point $ (y_l) $ can be reached locally. Such systems are called exactly controllable.\n", "\n", "\n", "There are many different notions of controllability but all are related to Lie brackets on the one hand ('geometric condition' or Hörmander condition) and certain analytic assumptions on the vector fields on the other hand." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can we reach with our particular vector fields the geometric condition? We consider some illustrative examples of course for $ d \\geq 2 $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Take a one point training set and choose $ V_i(x) = A_i x $ to be linear vector fields, e.g. with respect to a standard normal distribution on the matrix elements. Then the geometric condition (Hörmander condition) is satisfied at any point $ x \\neq 0 $ with probability $1$.\n", "2. Take a one point traning set and choose $V_i $ one layer neural networks with randomly choosen directions $ \\mu_j $ and activation functions with non-vanishing derivative at all points, then the geometric condition is satisfied.\n", "3. Take a general $ \\operatorname{card}(L) $ point training set and consider $ d = m \\times \\operatorname{card} $ control directions and vector fields. Assume that for every point $ x_l $ in the training set there are $m$ linearly independent vector fields. Then the geometric condition is satisfied.\n", "4. We consider this question also in an infinite dimensional situation, for instance on the torus: for every $ m \\geq 1 $ for \\emph{every} $ d \\geq 2 $ there are vector fields $ V_i $ on the torus such that the transport operators\n", "$$\n", "V_i f(x) = d f(x) \\cdot V(x)\n", "$$\n", "on $L^2(\\mathbb{T}^m)$ generate with their Lie brackets a dense subspace at any non-constant Fourier polynomial $ f \\in L^2(\\mathbb{T}^m) $. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having this theoretical result at hand we know that there are actually 'generic' choices of vector fields (often realized by random choices) which at least locally allow for controllability.\n", "\n", "The actual interpretation (which is another universal approximation theorem) is the following: a generic choice of vector fields $ V_i $ of the above controlled system can be realized by random neural networks and a certain number of controls $ u^1,\\ldots,u^d$ leads to an infinite depth neural network with $ m + N $ nodes on each layer which can approximate any continuous function on a compact set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This might help to explain the following curious phenomenon (we remember the standard MNIST classification from last week's lecture 1):" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "#matplotlib inline # Only use this if using iPython\n", "image_index = np.random.randint(60000) #7777 # You may select anything up to 60,000\n", "print(y_train[image_index]) # The label is printed\n", "plt.imshow(x_train[image_index], cmap='Greys')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x_train shape: (60000, 28, 28, 1)\n", "Number of images in x_train 60000\n", "Number of images in x_test 10000\n" ] }, { "data": { "text/plain": [ "(60000,)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Reshaping the array to 4-dims so that it can work with the Keras API\n", "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n", "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n", "input_shape = (28, 28, 1)\n", "# Making sure that the values are float so that we can get decimal points after division\n", "x_train = x_train.astype('float32')\n", "x_test = x_test.astype('float32')\n", "# Normalizing the RGB codes by dividing it to the max RGB value.\n", "x_train /= 255\n", "x_test /= 255\n", "print('x_train shape:', x_train.shape)\n", "print('Number of images in x_train', x_train.shape[0])\n", "print('Number of images in x_test', x_test.shape[0])\n", "y_train.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /scratch/users/jteichma/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1205: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "keep_dims is deprecated, use keepdims instead\n" ] } ], "source": [ "# Importing the required Keras modules containing model and layers\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D\n", "# Creating a Sequential Model and adding the layers\n", "model = Sequential()\n", "layer1=Conv2D(28, kernel_size=(3,3), input_shape=input_shape)\n", "layer1.trainable=True\n", "model.add(layer1) # (3,3) for mnist\n", "model.add(MaxPooling2D(pool_size=(2, 2))) # (2,2) for mnist\n", "model.add(Flatten()) # Flattening the 2D arrays for fully connected layers\n", "layer2 = Dense(128, activation=tf.nn.relu,kernel_initializer='random_uniform')#glorot_uniform is by default\n", "layer2.trainable=True#False\n", "model.add(layer2)\n", "#layer3 = Dense(2*128, activation=tf.nn.relu,kernel_initializer='random_uniform')\n", "#layer3.trainable=False\n", "#model.add(layer3)\n", "model.add(Dropout(0.2))\n", "model.add(Dense(10,activation=tf.nn.softmax))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_1 (Conv2D) (None, 26, 26, 28) 280 \n", "_________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 28) 0 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 4732) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 128) 605824 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 128) 0 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 10) 1290 \n", "=================================================================\n", "Total params: 607,394\n", "Trainable params: 607,394\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /scratch/users/jteichma/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1290: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "keep_dims is deprecated, use keepdims instead\n", "WARNING:tensorflow:From /scratch/users/jteichma/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1154: calling reduce_max (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "keep_dims is deprecated, use keepdims instead\n", "Epoch 1/1\n", "60000/60000 [==============================] - 45s - loss: 0.2034 - acc: 0.9383 \n", " 9952/10000 [============================>.] - ETA: 0s\n", " [0.07407166879139841, 0.9771]\n" ] } ], "source": [ "model.compile(optimizer='adam', \n", " loss='sparse_categorical_crossentropy', \n", " metrics=['accuracy'])\n", "for i in range(1):\n", " model.fit(x=x_train,y=y_train, epochs=1)\n", " x = model.evaluate(x_test, y_test)\n", " print('\\n',x)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras import initializers\n", "\n", "model = Sequential()\n", "layer1=Dense(128,activation=tf.nn.relu, input_shape=input_shape)#128\n", "layer1.trainable=False\n", "model.add(layer1)\n", "layer2=Dense(32,activation=tf.nn.relu)#128\n", "layer2.trainable=False\n", "model.add(layer2)\n", "#model.add(Flatten())\n", "#layer4=Dense(128,activation=tf.nn.relu)\n", "#layer4.trainable=False\n", "#layer5=ConvexCombination()([layer2,layer4])\n", "for i in range(3):\n", " layer3=Dense(128,activation=tf.nn.relu,kernel_initializer=initializers.random_normal(0,0.1)) \n", " layer3.trainable=False\n", " model.add(layer3)\n", "#model.add(MaxPooling2D(pool_size=(2, 2))) # (2,2) for mnist\n", "model.add(Flatten()) # Flattening the 2D arrays for fully connected layers\n", "#for i in range(5):\n", "# layer3 = Dense(128, activation=tf.nn.relu,kernel_initializer='random_uniform')#glorot_uniform is by default\n", "# layer3.trainable=False\n", "# model.add(layer3)\n", "#layer3 = Dense(2*128, activation=tf.nn.relu,kernel_initializer='random_uniform')\n", "#layer3.trainable=False\n", "#model.add(layer3)\n", "#model.add(Dropout(0.2))\n", "#layer4=Dense(100,activation='linear')\n", "#layer4.trainable=False\n", "#model.add(layer4)\n", "model.add(Dense(10,activation=tf.nn.softmax))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_116 (Dense) (None, 28, 28, 128) 256 \n", "_________________________________________________________________\n", "dense_117 (Dense) (None, 28, 28, 32) 4128 \n", "_________________________________________________________________\n", "dense_118 (Dense) (None, 28, 28, 128) 4224 \n", "_________________________________________________________________\n", "dense_119 (Dense) (None, 28, 28, 128) 16512 \n", "_________________________________________________________________\n", "dense_120 (Dense) (None, 28, 28, 128) 16512 \n", "_________________________________________________________________\n", "flatten_18 (Flatten) (None, 100352) 0 \n", "_________________________________________________________________\n", "dense_121 (Dense) (None, 10) 1003530 \n", "=================================================================\n", "Total params: 1,045,162\n", "Trainable params: 1,003,530\n", "Non-trainable params: 41,632\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "60000/60000 [==============================] - 394s - loss: 0.4408 - acc: 0.8862 \n", "10000/10000 [==============================] - 52s \n", "\n", " [0.30022240661382676, 0.9167]\n", "Epoch 1/1\n", "60000/60000 [==============================] - 391s - loss: 0.2980 - acc: 0.9170 \n", "10000/10000 [==============================] - 53s \n", "\n", " [0.2787766061902046, 0.9214]\n", "Epoch 1/1\n", "60000/60000 [==============================] - 388s - loss: 0.2795 - acc: 0.9210 \n", "10000/10000 [==============================] - 53s \n", "\n", " [0.2696303952664137, 0.9242]\n", "Epoch 1/1\n", "60000/60000 [==============================] - 384s - loss: 0.2704 - acc: 0.9242 \n", "10000/10000 [==============================] - 51s \n", "\n", " [0.2721088460594416, 0.9251]\n", "Epoch 1/1\n", "60000/60000 [==============================] - 387s - loss: 0.2647 - acc: 0.9262 \n", "10000/10000 [==============================] - 55s \n", "\n", " [0.26890463502407075, 0.9235]\n", "Epoch 1/1\n", "60000/60000 [==============================] - 382s - loss: 0.2600 - acc: 0.9276 - ETA: 3s - loss: 0.259\n", "10000/10000 [==============================] - 53s \n", "\n", " [0.2688384540170431, 0.9259]\n", "Epoch 1/1\n", "60000/60000 [==============================] - 391s - loss: 0.2566 - acc: 0.9287 \n", "10000/10000 [==============================] - 52s \n", "\n", " [0.2709984438627958, 0.9264]\n", "Epoch 1/1\n", "60000/60000 [==============================] - 391s - loss: 0.2540 - acc: 0.9296 \n", "10000/10000 [==============================] - 53s \n", "\n", " [0.2688856121197343, 0.926]\n", "Epoch 1/1\n", "60000/60000 [==============================] - 327s - loss: 0.2517 - acc: 0.9305 \n", " 9984/10000 [============================>.] - ETA: 0s\n", " [0.2685184384942055, 0.9268]\n", "Epoch 1/1\n", "60000/60000 [==============================] - 198s - loss: 0.2500 - acc: 0.9309 \n", " 9984/10000 [============================>.] - ETA: 0s\n", " [0.266049697086215, 0.926]\n" ] } ], "source": [ "model.compile(optimizer='adam', \n", " loss='sparse_categorical_crossentropy', \n", " metrics=['accuracy'])\n", "for i in range(10):\n", " model.fit(x=x_train,y=y_train, epochs=1)\n", " x = model.evaluate(x_test, y_test)\n", " print('\\n',x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We realize: if we replace the well specified architecture by a random one also obtain reasonable training results (even though speed deteriorates). Of course this is an extremely generic implementation, however, it shows the effect." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 0\n", "Grand Truth 2\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 8\n", "Grand Truth 6\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 7\n", "Grand Truth 2\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 9\n", "Grand Truth 5\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 9\n", "Grand Truth 7\n" ] }, { "data": { "image/png": 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c+7VxLh2HAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 3\n", "Grand Truth 5\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 8\n", "Grand Truth 3\n" ] }, { "data": { "image/png": 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Q/w/3aObRgM6uvgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 3\n", "Grand Truth 5\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 8\n", "Grand Truth 3\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction 1\n", "Grand Truth 7\n", "0.1\n" ] } ], "source": [ "img_rows=28\n", "img_cols=28\n", "count = 0\n", "for i in range(100):\n", " image_index = np.random.randint(10000)\n", " pred = model.predict(x_test[image_index].reshape(1, img_rows, img_cols, 1))\n", " if pred.argmax() != y_test[image_index]:\n", " plt.imshow(x_test[image_index].reshape(28, 28),cmap='Greys')\n", " plt.show()\n", " print('Prediction',pred.argmax())\n", " print('Grand Truth',y_test[image_index])\n", " count = count + 1\n", " #break\n", "print(count/100)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "In order to understand actual training we shall derive the first variation with respect to parameters $ \\theta $, i.e. we take the derivative with respect to the parameter $ \\theta $, denoted by $ \\partial X_T $ of\n", "$$\n", "dX^\\theta_t = \\sum_{i=1}^d V^\\theta_i(t,X^\\theta_t) du^i(t), \\; X_0 = x \\; .\n", "$$\n", "Notice that we write for convenience an additional time dependence in the vector fields since this is actually an important case. This leads to\n", "$$\n", "d \\partial X^\\theta_t = \\sum_{i=1}^d \\big( \\partial V^\\theta_i(t, X^\\theta_t) + dV^\\theta_i(t, X^\\theta_t) \\partial X^\\theta_t \\big) du^i(t), \\; \\partial X^\\theta_0 = 0 \\, ,\n", "$$\n", "which can be solved by first solving the equation for $ X^\\theta $ in a forward manner, i.e.\n", "$$\n", "X^\\theta_t = x + \\sum_{i=1}^d \\int_0^t V^\\theta_i(s,X^\\theta_s) du^i(s) \\,\n", "$$\n", "for $ t \\geq 0 $, and observing that the solution of the first variation equation is actually\n", "given by an evolution operator $ J_{s,T} $ which satisfies with respect to $s$\n", "$$\n", "d J_{s,T} = - \\sum_{i=1}^d dV^\\theta_i(s,X^\\theta_s) J_{s,T} du^i(s), \\; J_{T,T} = \\operatorname{id}\n", "$$\n", "and the variation of constants formula\n", "$$\n", "\\partial X^\\theta_T = \\sum_{i=1}^d \\int_0^T J_{s,T} \\partial V^\\theta_i(s,X^\\theta_s) du^i(s) \\, .\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These remarkable formulas can be interpreted in the following way: calculate first $ J_{s,T} $ which is calculated backwards in time along the solution trajectory of the forward process $ X^\\theta $ on $[0,t]$. Then integrate the derivatives of $ \\partial V^\\theta $ transformed by $ J_{s,T} $ to obtain the derivative of $ \\partial X_T^\\theta $\n", "$$\n", "\\partial X^\\theta_T = \\sum_{i=1}^d \\int_0^T J_{s,T} \\partial V^\\theta_i(s,X^\\theta_s) du^i(s) \\, .\n", "$$\n", "If instead one is interested in the gradient of a loss function $ L(X^\\theta_T) $, by $ \\partial L(X^\\theta_T) = \\nabla L(X_T^\\theta) \\partial X^\\theta_T $ the problem is solved, too." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our approach we do also consider the controls $ u^i $ as variables which can be optimized, hence also the gradients with respect to $ u^i $ are of importance: we calculate the derivative of $ u^i + \\epsilon a^i $ for $ \\epsilon = 0 $ and obtain\n", "$$\n", "d \\partial_\\epsilon X_T^\\theta = \\sum_{i=1}^d V^\\theta_i(t, X^\\theta_t) da^i(t) + dV^\\theta_i(t, X^\\theta_t) \\partial_\\epsilon X^\\theta_t du^i(t), \\; \\partial_\\epsilon X^\\theta_0 = 0 \\, ,\n", "$$\n", "hence a completely analogue situation appears, i.e.\n", "$$\n", "\\partial_\\epsilon X_T^\\theta = \\sum_{i=1}^d \\int_0^t J_{s,T} V^\\theta_i(s, X^\\theta_s) da^i(s)\n", "$$\n", "by variation of constants." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">Notice that the surjectivity of the map $ a \\mapsto \\partial_\\epsilon X^\\theta_T $ leads precisely to a Hörmander condition for the vector fields $ V_i $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us sum up the previous considerations:\n", "1. We can understand deep neural networks as flows of controlled ordinary differential equations.\n", "2. Backpropagation appears as the classical forward-backward calculation of first variations.\n", "3. Variations with respect to the control can also be calculated in a forward-backward manner.\n", "4. Since gradients can be efficiently calculated we can set up search algorithms stochastic gradient descent, simulated annealing, etc." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Let us consider this for one layer of a neural network:\n", "$$\n", "L^{(i)}: x \\mapsto W^{(i)}x + a^{(0)} \\mapsto \\phi (W^{(i)}x + a^{(0)})\n", "$$\n", "Translating this to a controlled ODE needs the following ingredients:\n", "1. a control $u(t)=1_{[1,2[}(t)+2 \\, 1_{[2,\\infty[}(t)$, i.e. we have two jumps by $1$ at $ t = 1 $ and $ t =2 $ of size $1$, and\n", "2. a time-dependent vector field\n", "$$\n", "V(t,x) = 1_{[0,1]}(t)(L^{(0)}(x)-x)+ 1_{]1,\\infty]}(t)(L^{(1)}(x) -x ) \\, .\n", "$$\n", "\n", "The corresponding neural network at 'time' $ 3 $ is just\n", "$$\n", "x \\mapsto L^{(0)}(x) \\mapsto \\phi(W^{(1)}L^{(0)}(x) + a^{(1)}) \\,\n", "$$\n", "which is a one hidden layer neural network with linear readout.\n", "\n", "The parameters $ \\theta $ are the matrix weights $ W^{(i)} $ and shifts $ a^{(i)} $.\n", "\n", "The backwards first variation can be constructed by looking at the derivatives of $ V(t,.) $ with respect to $ x $ and solving the corresponding equation backwards: at 'time' $T=3$ we have\n", "$$\n", "J_{s,3} v =v + 1_{[0,2[}(s)(dL^{(1)}(x)v-v) +1_{[0,1[}(s)(d L^{(0)}(x)v-v) \n", "$$\n", "which is nothing than encoding the outer part of the chain rule.\n", "\n", "Calculating now the derivative of the network with respect to, e.g., $ a^{(1)} $ amounts then just to calculting the variation of constants integral which gives the correct partial derivatives." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us look at this phenomenon in an example following [Deep Learning with Keras and Tensorflow](https://github.com/leriomaggio). We consider a one hidden layer neural network as above with loss function $ 0.5*(y-\\hat{y})^2 $ whose gradient is easily calculated in terms of the scalar product of the error with the respective gradient of $ X^\\theta $ with respect to the network parameters.\n", "\n", "What is implemented in the sequel is a simple gradient descent (with momentum): let us denote the loss function by $ L(\\theta) $, then\n", "$$\n", "\\theta^{(n+1)} = \\theta^{(n)} - N \\, \\nabla L(\\theta^{(n)}) - M \\, \\nabla L(\\theta^{(n-1)}) \\, .\n", "$$" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import the required packages\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import scipy\n", "\n", "# Display plots in notebook \n", "%matplotlib inline\n", "# Define plot's default figure size\n", "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n", "\n", "import random\n", "random.seed(123)\n", "\n", "# calculate a random number where: a <= rand < b\n", "def rand(a, b):\n", " return (b-a)*random.random() + a" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 2)\n", "(500,)\n" ] } ], "source": [ "#read the datasets\n", "train = pd.read_csv(\"data/intro_to_ann.csv\")\n", "\n", "X, y = np.array(train.ix[:,0:2]), np.array(train.ix[:,2])\n", "\n", "print(X.shape)\n", "print(y.shape)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CEgIVWSVJWrtp/bD70hEYjMboayzLoqB3HtxdedLudm78dwdVhrcUrMNVpXBZnJ2z0yHx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\nhPiszv0yDNNimC4lHMDKLqhChH6XLd0frBNRo7IW1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Let's plot the dataset and see how it is\n", "plt.scatter(X[:,0], X[:,1], s=40, c=1-y, cmap=plt.cm.BuGn)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Make a matrix \n", "def makeMatrix(I, J, fill=0.0):\n", " return np.zeros([I,J])" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# our sigmoid function\n", "def sigmoid(x):\n", " #return math.tanh(x)\n", " return 1/(1+np.exp(-x))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# derivative of our sigmoid function, in terms of the output (i.e. y)\n", "def dsigmoid(y):\n", " return y - y**2" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# the multilayerperceptron class (with one hidden layer)\n", "\n", "class MLP:\n", " def __init__(self, ni, nh, no):\n", " # number of input, hidden, and output nodes\n", " self.ni = ni + 1 # +1 for bias node\n", " self.nh = nh\n", " self.no = no\n", "\n", " # activations for nodes -- these are list of respective length\n", " self.ai = [1.0]*self.ni\n", " self.ah = [1.0]*self.nh\n", " self.ao = [1.0]*self.no\n", " \n", " # create weights\n", " self.wi = makeMatrix(self.ni, self.nh)\n", " self.wo = makeMatrix(self.nh, self.no)\n", " \n", " # set them to random vaules\n", " for i in range(self.ni):\n", " for j in range(self.nh):\n", " self.wi[i][j] = rand(-0.2, 0.2)\n", " for j in range(self.nh):\n", " for k in range(self.no):\n", " self.wo[j][k] = rand(-2.0, 2.0)\n", "\n", " # last change in weights for momentum \n", " self.ci = makeMatrix(self.ni, self.nh)\n", " self.co = makeMatrix(self.nh, self.no)\n", " \n", "\n", " def backPropagate(self, targets, N, M):\n", " \n", " if len(targets) != self.no:\n", " print(targets)\n", " raise ValueError('wrong number of target values')\n", "\n", " # calculate error terms for output\n", " output_deltas = np.zeros(self.no)\n", " for k in range(self.no):\n", " error = targets[k]-self.ao[k] \n", " output_deltas[k] = dsigmoid(self.ao[k]) * error\n", "\n", " # calculate error terms for hidden\n", " hidden_deltas = np.zeros(self.nh)\n", " for j in range(self.nh):\n", " error = 0.0\n", " for k in range(self.no):\n", " error += output_deltas[k]*self.wo[j][k]\n", " hidden_deltas[j] = dsigmoid(self.ah[j]) * error\n", "\n", " # update output weights\n", " for j in range(self.nh):\n", " for k in range(self.no):\n", " change = output_deltas[k] * self.ah[j]\n", " self.wo[j][k] += N*change + M*self.co[j][k] # N is the learning rate, M the momentum\n", " self.co[j][k] = change\n", "\n", " # update input weights\n", " for i in range(self.ni):\n", " for j in range(self.nh):\n", " change = hidden_deltas[j]*self.ai[i]\n", " self.wi[i][j] += N*change + M*self.ci[i][j]\n", " self.ci[i][j] = change\n", "\n", " # calculate error\n", " error = 0.0\n", " for k in range(len(targets)):\n", " error += 0.5*(targets[k]-self.ao[k])**2\n", " return error\n", "\n", "\n", " def test(self, patterns):\n", " self.predict = np.empty([len(patterns), self.no])\n", " for i, p in enumerate(patterns):\n", " self.predict[i] = self.activate(p)\n", " #self.predict[i] = self.activate(p[0])\n", " \n", " def activate(self, inputs): # here the network is constructed as a function\n", " \n", " if len(inputs) != self.ni-1:\n", " print(inputs)\n", " raise ValueError('wrong number of inputs')\n", "\n", " # input activations\n", " for i in range(self.ni-1):\n", " self.ai[i] = inputs[i]\n", "\n", " # hidden activations\n", " for j in range(self.nh):\n", " sum_h = 0.0\n", " for i in range(self.ni):\n", " sum_h += self.ai[i] * self.wi[i][j]\n", " self.ah[j] = sigmoid(sum_h)\n", "\n", " # output activations\n", " for k in range(self.no):\n", " sum_o = 0.0\n", " for j in range(self.nh):\n", " sum_o += self.ah[j] * self.wo[j][k]\n", " self.ao[k] = sigmoid(sum_o)\n", "\n", " return self.ao[:]\n", " \n", "\n", " def train(self, patterns, iterations=1000, N=0.5, M=0.1):\n", " # N: learning rate\n", " # M: momentum factor\n", " patterns = list(patterns)\n", " for i in range(iterations):\n", " error = 0.0\n", " for p in patterns:\n", " inputs = p[0]\n", " targets = p[1]\n", " self.activate(inputs)\n", " error += self.backPropagate([targets], N, M)\n", " if i % 5 == 0:\n", " print('error in interation %d : %-.5f' % (i,error))\n", " print('Final training error: %-.5f' % error)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error in interation 0 : 31.50196\n", "Final training error: 31.50196\n", "91.5 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" ] } ], "source": [ "# create a network with two inputs, ten hidden, and one output nodes\n", "ann = MLP(2, 10, 1)\n", "\n", "%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=1)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Helper function to plot a decision boundary.\n", "# This generates the contour plot to show the decision boundary visually\n", "def plot_decision_boundary(nn_model):\n", " # Set min and max values and give it some padding\n", " x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", " y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", " h = 0.01\n", " # Generate a grid of points with distance h between them\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h), \n", " np.arange(y_min, y_max, h))\n", " # Predict the function value for the whole gid\n", " nn_model.test(np.c_[xx.ravel(), yy.ravel()])\n", " Z = nn_model.predict\n", " Z[Z>=0.5] = 1\n", " Z[Z<0.5] = 0\n", " Z = Z.reshape(xx.shape)\n", " # Plot the contour and training examples\n", " plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n", " plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.BuGn)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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kwz6t1eOS02N6rSC6N6SzTzHpew6jdivFXsNllVj+QMJtjsKbW98jVXjBSWTs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\n8EVERERkIYYvIiIiIgsxfBERERFZ6P8BnrhTeB06FLMAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_decision_boundary(ann)\n", "plt.title(\"Model with 1 iteration\")" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error in interation 0 : 29.18252\n", "Final training error: 29.18252\n", "Final training error: 25.14727\n", "Final training error: 24.99525\n", "Final training error: 24.96553\n", "Final training error: 24.94232\n", "error in interation 5 : 24.91374\n", "Final training error: 24.91374\n", "Final training error: 24.88046\n", "Final training error: 24.84468\n", "Final training error: 24.80822\n", "Final training error: 24.77233\n", "error in interation 10 : 24.73777\n", "Final training error: 24.73777\n", "Final training error: 24.70496\n", "Final training error: 24.67407\n", "Final training error: 24.64516\n", "Final training error: 24.61817\n", "error in interation 15 : 24.59302\n", "Final training error: 24.59302\n", "Final training error: 24.56961\n", "Final training error: 24.54783\n", "Final training error: 24.52756\n", "Final training error: 24.50869\n", "error in interation 20 : 24.49111\n", "Final training error: 24.49111\n", "Final training error: 24.47472\n", "Final training error: 24.45942\n", "Final training error: 24.44511\n", "Final training error: 24.43169\n", "error in interation 25 : 24.41908\n", "Final training error: 24.41908\n", "Final training error: 24.40718\n", "Final training error: 24.39591\n", "Final training error: 24.38517\n", "Final training error: 24.37487\n", "error in interation 30 : 24.36491\n", "Final training error: 24.36491\n", "Final training error: 24.35519\n", "Final training error: 24.34558\n", "Final training error: 24.33593\n", "Final training error: 24.32608\n", "error in interation 35 : 24.31578\n", "Final training error: 24.31578\n", "Final training error: 24.30473\n", "Final training error: 24.29249\n", "Final training error: 24.27840\n", "Final training error: 24.26147\n", "error in interation 40 : 24.24005\n", "Final training error: 24.24005\n", "Final training error: 24.21136\n", "Final training error: 24.17036\n", "Final training error: 24.10761\n", "Final training error: 24.00473\n", "error in interation 45 : 23.82632\n", "Final training error: 23.82632\n", "Final training error: 23.50945\n", "Final training error: 22.96422\n", "Final training error: 22.11245\n", "Final training error: 20.95999\n", "error in interation 50 : 19.62322\n", "Final training error: 19.62322\n", "Final training error: 18.25842\n", "Final training error: 16.97667\n", "Final training error: 15.82596\n", "Final training error: 14.81553\n", "error in interation 55 : 13.93791\n", "Final training error: 13.93791\n", "Final training error: 13.17903\n", "Final training error: 12.52233\n", "Final training error: 11.95116\n", "Final training error: 11.45073\n", "error in interation 60 : 11.00907\n", "Final training error: 11.00907\n", "Final training error: 10.61709\n", "Final training error: 10.26800\n", "Final training error: 9.95653\n", "Final training error: 9.67844\n", "error in interation 65 : 9.43003\n", "Final training error: 9.43003\n", "Final training error: 9.20805\n", "Final training error: 9.00956\n", "Final training error: 8.83190\n", "Final training error: 8.67268\n", "error in interation 70 : 8.52974\n", "Final training error: 8.52974\n", "Final training error: 8.40117\n", "Final training error: 8.28527\n", "Final training error: 8.18054\n", "Final training error: 8.08567\n", "error in interation 75 : 7.99952\n", "Final training error: 7.99952\n", "Final training error: 7.92109\n", "Final training error: 7.84953\n", "Final training error: 7.78406\n", "Final training error: 7.72406\n", "error in interation 80 : 7.66893\n", "Final training error: 7.66893\n", "Final training error: 7.61819\n", "Final training error: 7.57140\n", "Final training error: 7.52816\n", "Final training error: 7.48813\n", "error in interation 85 : 7.45100\n", "Final training error: 7.45100\n", "Final training error: 7.41651\n", "Final training error: 7.38440\n", "Final training error: 7.35446\n", "Final training error: 7.32650\n", "error in interation 90 : 7.30033\n", "Final training error: 7.30033\n", "Final training error: 7.27579\n", "Final training error: 7.25275\n", "Final training error: 7.23107\n", "Final training error: 7.21063\n", "error in interation 95 : 7.19134\n", "Final training error: 7.19134\n", "Final training error: 7.17310\n", "Final training error: 7.15582\n", "Final training error: 7.13942\n", "Final training error: 7.12384\n", "7.52 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" ] } ], "source": [ "# create a network with two inputs, ten hidden, and one output nodes\n", "ann = MLP(2, 10, 1)\n", "\n", "%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=100)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1SPKllJqllNqhlPoxyfGJSqkypdQP1X9ub4nnCiGEEJ3ZZOOqVIcgWkFLDTs+BzwGzG7g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\nP9XjpezVDyh79YPOuoUjDsli0gxL70PxeIk9aSgZd1zvEyN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GirJgW7PZ3xT9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_decision_boundary(ann)\n", "plt.title(\"Model with 100 iterations\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Several remarks are due here:\n", "1. There are different ways to convert time to depth leading to slightly different ways how to implement backpropagation. It is very efficient to have the 'continuous-time' formulas in mind.\n", "2. It might be also useful to think about controls being themselves trajectories of a stochastic process, for intance a generic semi-martingale.\n", "3. Take controls being $ d $ Brownian motion (with respect to Stratonovich integration) and assume a Hörmander condition valid for the particle system starting on the training data set, that is the ODE on $ \\mathbb{R}^{mL} $ given by\n", "$$\n", "dZ_t = \\sum_{i=1}^d W_i(Z_t) \\circ dB^i(t) \\, ,\n", "$$\n", "where\n", "$$\n", "(W_i((x_l)))_l:=V_i(x_l)\n", "$$\n", "for $ l \\in L $ and $ i = 1,\\ldots,d $. Then with probability one a (possibly only small) neighborhood of $ (x_l) $ is hit by the particle system. A random search can exhibit such trajectories and whence solve the supervised learning problem by simulation.\n", "4. One can also view this as a *deterministic mean field control* problem, where the controls *only* depend on the behavior of all particles." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the sequel the same implementation done in Keras." ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "modelann = Sequential()\n", "layer1=Dense(10,activation='sigmoid', input_shape=(2,))\n", "layer1.trainable=True\n", "modelann.add(layer1)\n", "layer2=Dense(1,activation='sigmoid')\n", "layer2.trainable=True\n", "modelann.add(layer2)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1000\n", "500/500 [==============================] - 0s - loss: 0.1062 - acc: 0.8540 \n", "Epoch 2/1000\n", "500/500 [==============================] - 0s - loss: 0.1059 - acc: 0.8560 \n", "Epoch 3/1000\n", "500/500 [==============================] - 0s - loss: 0.1054 - acc: 0.8560 \n", "Epoch 4/1000\n", "500/500 [==============================] - 0s - loss: 0.1051 - acc: 0.8560 \n", "Epoch 5/1000\n", "500/500 [==============================] - 0s - loss: 0.1047 - acc: 0.8560 \n", "Epoch 6/1000\n", "500/500 [==============================] - 0s - loss: 0.1045 - acc: 0.8560 \n", "Epoch 7/1000\n", "500/500 [==============================] - 0s - loss: 0.1041 - acc: 0.8580 \n", "Epoch 8/1000\n", "500/500 [==============================] - 0s - loss: 0.1038 - acc: 0.8580 \n", "Epoch 9/1000\n", "500/500 [==============================] - 0s - loss: 0.1035 - acc: 0.8580 \n", "Epoch 10/1000\n", "500/500 [==============================] - 0s - loss: 0.1033 - acc: 0.8580 \n", "Epoch 11/1000\n", "500/500 [==============================] - 0s - loss: 0.1030 - acc: 0.8600 \n", "Epoch 12/1000\n", "500/500 [==============================] - 0s - loss: 0.1028 - acc: 0.8580 \n", "Epoch 13/1000\n", "500/500 [==============================] - 0s - loss: 0.1025 - acc: 0.8640 \n", "Epoch 14/1000\n", "500/500 [==============================] - 0s - loss: 0.1023 - acc: 0.8660 \n", "Epoch 15/1000\n", "500/500 [==============================] - 0s - loss: 0.1021 - acc: 0.8660 \n", "Epoch 16/1000\n", "500/500 [==============================] - 0s - loss: 0.1018 - acc: 0.8660 \n", "Epoch 17/1000\n", "500/500 [==============================] - 0s - loss: 0.1016 - acc: 0.8680 \n", "Epoch 18/1000\n", "500/500 [==============================] - 0s - loss: 0.1015 - acc: 0.8680 \n", "Epoch 19/1000\n", "500/500 [==============================] - 0s - loss: 0.1013 - acc: 0.8680 \n", "Epoch 20/1000\n", "500/500 [==============================] - 0s - loss: 0.1011 - acc: 0.8660 \n", "Epoch 21/1000\n", "500/500 [==============================] - 0s - loss: 0.1009 - acc: 0.8680 \n", "Epoch 22/1000\n", "500/500 [==============================] - 0s - loss: 0.1008 - acc: 0.8680 \n", "Epoch 23/1000\n", "500/500 [==============================] - 0s - loss: 0.1006 - acc: 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0.8700 \n", "Epoch 34/1000\n", "500/500 [==============================] - 0s - loss: 0.0993 - acc: 0.8680 \n", "Epoch 35/1000\n", "500/500 [==============================] - 0s - loss: 0.0991 - acc: 0.8680 \n", "Epoch 36/1000\n", "500/500 [==============================] - 0s - loss: 0.0990 - acc: 0.8680 \n", "Epoch 37/1000\n", "500/500 [==============================] - 0s - loss: 0.0990 - acc: 0.8680 \n", "Epoch 38/1000\n", "500/500 [==============================] - 0s - loss: 0.0989 - acc: 0.8680 \n", "Epoch 39/1000\n", "500/500 [==============================] - 0s - loss: 0.0988 - acc: 0.8680 \n", "Epoch 40/1000\n", "500/500 [==============================] - 0s - loss: 0.0987 - acc: 0.8680 \n", "Epoch 41/1000\n", "500/500 [==============================] - 0s - loss: 0.0987 - acc: 0.8660 \n", "Epoch 42/1000\n", "500/500 [==============================] - 0s - loss: 0.0986 - acc: 0.8660 \n", "Epoch 43/1000\n", "500/500 [==============================] - 0s - loss: 0.0985 - acc: 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acc: 0.8700 \n", "Epoch 221/1000\n", "500/500 [==============================] - 0s - loss: 0.0952 - acc: 0.8700 \n", "Epoch 222/1000\n", "500/500 [==============================] - 0s - loss: 0.0952 - acc: 0.8700 \n", "Epoch 223/1000\n", "500/500 [==============================] - 0s - loss: 0.0952 - acc: 0.8700 \n", "Epoch 224/1000\n", "500/500 [==============================] - 0s - loss: 0.0951 - acc: 0.8700 \n", "Epoch 225/1000\n", "500/500 [==============================] - 0s - loss: 0.0952 - acc: 0.8700 \n", "Epoch 226/1000\n", "500/500 [==============================] - 0s - loss: 0.0951 - acc: 0.8700 \n", "Epoch 227/1000\n", "500/500 [==============================] - 0s - loss: 0.0950 - acc: 0.8700 \n", "Epoch 228/1000\n", "500/500 [==============================] - 0s - loss: 0.0950 - acc: 0.8700 \n", "Epoch 229/1000\n", "500/500 [==============================] - 0s - loss: 0.0950 - acc: 0.8700 \n", "Epoch 230/1000\n", "500/500 [==============================] - 0s - loss: 0.0950 - acc: 0.8700 \n", "Epoch 231/1000\n", "500/500 [==============================] - 0s - loss: 0.0949 - acc: 0.8700 \n", "Epoch 232/1000\n", "500/500 [==============================] - 0s - loss: 0.0948 - acc: 0.8700 \n", "Epoch 233/1000\n", "500/500 [==============================] - 0s - loss: 0.0949 - acc: 0.8700 \n", "Epoch 234/1000\n", "500/500 [==============================] - 0s - loss: 0.0948 - acc: 0.8700 \n", "Epoch 235/1000\n", "500/500 [==============================] - 0s - loss: 0.0947 - acc: 0.8700 \n", "Epoch 236/1000\n", "500/500 [==============================] - 0s - loss: 0.0947 - acc: 0.8700 \n", "Epoch 237/1000\n", "500/500 [==============================] - 0s - loss: 0.0947 - acc: 0.8700 \n", "Epoch 238/1000\n", "500/500 [==============================] - 0s - loss: 0.0946 - acc: 0.8700 \n", "Epoch 239/1000\n", "500/500 [==============================] - 0s - loss: 0.0946 - acc: 0.8700 \n", "Epoch 240/1000\n", "500/500 [==============================] - 0s - loss: 0.0945 - acc: 0.8700 \n", "Epoch 241/1000\n", "500/500 [==============================] - 0s - loss: 0.0945 - acc: 0.8720 \n", "Epoch 242/1000\n", "500/500 [==============================] - 0s - loss: 0.0944 - acc: 0.8700 \n", "Epoch 243/1000\n", "500/500 [==============================] - 0s - loss: 0.0944 - acc: 0.8700 \n", "Epoch 244/1000\n", "500/500 [==============================] - 0s - loss: 0.0944 - acc: 0.8720 \n", "Epoch 245/1000\n", "500/500 [==============================] - 0s - loss: 0.0943 - acc: 0.8740 \n", "Epoch 246/1000\n", "500/500 [==============================] - 0s - loss: 0.0942 - acc: 0.8740 \n", "Epoch 247/1000\n", "500/500 [==============================] - 0s - loss: 0.0942 - acc: 0.8740 \n", "Epoch 248/1000\n", "500/500 [==============================] - 0s - loss: 0.0941 - acc: 0.8720 \n", "Epoch 249/1000\n", "500/500 [==============================] - 0s - loss: 0.0941 - acc: 0.8740 \n", "Epoch 250/1000\n", "500/500 [==============================] - 0s - loss: 0.0940 - acc: 0.8740 \n", "Epoch 251/1000\n", "500/500 [==============================] - 0s - loss: 0.0940 - acc: 0.8740 \n", "Epoch 252/1000\n", "500/500 [==============================] - 0s - loss: 0.0939 - acc: 0.8740 \n", "Epoch 253/1000\n", "500/500 [==============================] - 0s - loss: 0.0939 - acc: 0.8740 \n", "Epoch 254/1000\n", "500/500 [==============================] - 0s - loss: 0.0939 - acc: 0.8720 \n", "Epoch 255/1000\n", "500/500 [==============================] - 0s - loss: 0.0938 - acc: 0.8740 \n", "Epoch 256/1000\n", "500/500 [==============================] - 0s - loss: 0.0938 - acc: 0.8740 \n", "Epoch 257/1000\n", "500/500 [==============================] - 0s - loss: 0.0937 - acc: 0.8740 \n", "Epoch 258/1000\n", "500/500 [==============================] - 0s - loss: 0.0936 - acc: 0.8740 \n", "Epoch 259/1000\n", "500/500 [==============================] - 0s - loss: 0.0936 - acc: 0.8740 \n", "Epoch 260/1000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "500/500 [==============================] - 0s - loss: 0.0935 - acc: 0.8740 \n", "Epoch 261/1000\n", "500/500 [==============================] - 0s - loss: 0.0934 - acc: 0.8740 \n", "Epoch 262/1000\n", "500/500 [==============================] - 0s - loss: 0.0934 - acc: 0.8740 \n", "Epoch 263/1000\n", "500/500 [==============================] - 0s - loss: 0.0933 - acc: 0.8740 \n", "Epoch 264/1000\n", "500/500 [==============================] - 0s - loss: 0.0933 - acc: 0.8740 \n", "Epoch 265/1000\n", "500/500 [==============================] - 0s - loss: 0.0932 - acc: 0.8740 \n", "Epoch 266/1000\n", "500/500 [==============================] - 0s - loss: 0.0932 - acc: 0.8740 \n", "Epoch 267/1000\n", "500/500 [==============================] - 0s - loss: 0.0931 - acc: 0.8740 \n", "Epoch 268/1000\n", "500/500 [==============================] - 0s - loss: 0.0930 - acc: 0.8740 \n", "Epoch 269/1000\n", "500/500 [==============================] - 0s - loss: 0.0930 - acc: 0.8740 \n", "Epoch 270/1000\n", "500/500 [==============================] - 0s - loss: 0.0929 - acc: 0.8740 \n", "Epoch 271/1000\n", "500/500 [==============================] - 0s - loss: 0.0928 - acc: 0.8740 \n", "Epoch 272/1000\n", "500/500 [==============================] - 0s - loss: 0.0928 - acc: 0.8740 \n", "Epoch 273/1000\n", "500/500 [==============================] - 0s - loss: 0.0927 - acc: 0.8740 \n", "Epoch 274/1000\n", "500/500 [==============================] - 0s - loss: 0.0926 - acc: 0.8740 \n", "Epoch 275/1000\n", "500/500 [==============================] - 0s - loss: 0.0926 - acc: 0.8740 \n", "Epoch 276/1000\n", "500/500 [==============================] - 0s - loss: 0.0926 - acc: 0.8740 \n", "Epoch 277/1000\n", "500/500 [==============================] - 0s - loss: 0.0925 - acc: 0.8740 \n", "Epoch 278/1000\n", "500/500 [==============================] - 0s - loss: 0.0924 - acc: 0.8740 \n", "Epoch 279/1000\n", "500/500 [==============================] - 0s - loss: 0.0923 - acc: 0.8740 \n", "Epoch 280/1000\n", "500/500 [==============================] - 0s - loss: 0.0922 - acc: 0.8740 \n", "Epoch 281/1000\n", "500/500 [==============================] - 0s - loss: 0.0922 - acc: 0.8740 \n", "Epoch 282/1000\n", "500/500 [==============================] - 0s - loss: 0.0921 - acc: 0.8740 \n", "Epoch 283/1000\n", "500/500 [==============================] - 0s - loss: 0.0921 - acc: 0.8760 \n", "Epoch 284/1000\n", "500/500 [==============================] - 0s - loss: 0.0920 - acc: 0.8760 \n", "Epoch 285/1000\n", "500/500 [==============================] - 0s - loss: 0.0919 - acc: 0.8740 \n", "Epoch 286/1000\n", "500/500 [==============================] - 0s - loss: 0.0918 - acc: 0.8740 \n", "Epoch 287/1000\n", "500/500 [==============================] - 0s - loss: 0.0918 - acc: 0.8740 \n", "Epoch 288/1000\n", "500/500 [==============================] - 0s - loss: 0.0917 - acc: 0.8740 \n", "Epoch 289/1000\n", "500/500 [==============================] - 0s - loss: 0.0916 - acc: 0.8760 \n", "Epoch 290/1000\n", "500/500 [==============================] - 0s - loss: 0.0915 - acc: 0.8760 \n", "Epoch 291/1000\n", "500/500 [==============================] - 0s - loss: 0.0915 - acc: 0.8760 \n", "Epoch 292/1000\n", "500/500 [==============================] - 0s - loss: 0.0914 - acc: 0.8760 \n", "Epoch 293/1000\n", "500/500 [==============================] - 0s - loss: 0.0913 - acc: 0.8760 \n", "Epoch 294/1000\n", "500/500 [==============================] - 0s - loss: 0.0912 - acc: 0.8760 \n", "Epoch 295/1000\n", "500/500 [==============================] - 0s - loss: 0.0912 - acc: 0.8760 \n", "Epoch 296/1000\n", "500/500 [==============================] - 0s - loss: 0.0911 - acc: 0.8760 \n", "Epoch 297/1000\n", "500/500 [==============================] - 0s - loss: 0.0910 - acc: 0.8760 \n", "Epoch 298/1000\n", "500/500 [==============================] - 0s - loss: 0.0909 - acc: 0.8760 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acc: 0.8780 \n", "Epoch 309/1000\n", "500/500 [==============================] - 0s - loss: 0.0900 - acc: 0.8780 \n", "Epoch 310/1000\n", "500/500 [==============================] - 0s - loss: 0.0900 - acc: 0.8760 \n", "Epoch 311/1000\n", "500/500 [==============================] - 0s - loss: 0.0899 - acc: 0.8780 \n", "Epoch 312/1000\n", "500/500 [==============================] - 0s - loss: 0.0898 - acc: 0.8760 \n", "Epoch 313/1000\n", "500/500 [==============================] - 0s - loss: 0.0898 - acc: 0.8820 \n", "Epoch 314/1000\n", "500/500 [==============================] - 0s - loss: 0.0896 - acc: 0.8820 \n", "Epoch 315/1000\n", "500/500 [==============================] - 0s - loss: 0.0896 - acc: 0.8800 \n", "Epoch 316/1000\n", "500/500 [==============================] - 0s - loss: 0.0895 - acc: 0.8800 \n", "Epoch 317/1000\n", "500/500 [==============================] - 0s - loss: 0.0894 - acc: 0.8820 \n", "Epoch 318/1000\n", "500/500 [==============================] - 0s - loss: 0.0893 - acc: 0.8820 \n", "Epoch 319/1000\n", "500/500 [==============================] - 0s - loss: 0.0892 - acc: 0.8820 \n", "Epoch 320/1000\n", "500/500 [==============================] - 0s - loss: 0.0891 - acc: 0.8820 \n", "Epoch 321/1000\n", "500/500 [==============================] - 0s - loss: 0.0890 - acc: 0.8820 \n", "Epoch 322/1000\n", "500/500 [==============================] - 0s - loss: 0.0890 - acc: 0.8840 \n", "Epoch 323/1000\n", "500/500 [==============================] - 0s - loss: 0.0889 - acc: 0.8820 \n", "Epoch 324/1000\n", "500/500 [==============================] - 0s - loss: 0.0888 - acc: 0.8820 \n", "Epoch 325/1000\n", "500/500 [==============================] - 0s - loss: 0.0887 - acc: 0.8820 \n", "Epoch 326/1000\n", "500/500 [==============================] - 0s - loss: 0.0886 - acc: 0.8840 \n", "Epoch 327/1000\n", "500/500 [==============================] - 0s - loss: 0.0885 - acc: 0.8840 \n", "Epoch 328/1000\n", "500/500 [==============================] - 0s - loss: 0.0884 - acc: 0.8840 \n", "Epoch 329/1000\n", "500/500 [==============================] - 0s - loss: 0.0883 - acc: 0.8840 \n", "Epoch 330/1000\n", "500/500 [==============================] - 0s - loss: 0.0882 - acc: 0.8840 \n", "Epoch 331/1000\n", "500/500 [==============================] - 0s - loss: 0.0882 - acc: 0.8840 \n", "Epoch 332/1000\n", "500/500 [==============================] - 0s - loss: 0.0881 - acc: 0.8840 \n", "Epoch 333/1000\n", "500/500 [==============================] - 0s - loss: 0.0880 - acc: 0.8840 \n", "Epoch 334/1000\n", "500/500 [==============================] - 0s - loss: 0.0879 - acc: 0.8840 \n", "Epoch 335/1000\n", "500/500 [==============================] - ETA: 0s - loss: 0.0373 - acc: 0.968 - 0s - loss: 0.0878 - acc: 0.8840 \n", "Epoch 336/1000\n", "500/500 [==============================] - 0s - loss: 0.0877 - acc: 0.8840 \n", "Epoch 337/1000\n", "500/500 [==============================] - 0s - loss: 0.0876 - acc: 0.8840 \n", "Epoch 338/1000\n", "500/500 [==============================] - 0s - loss: 0.0876 - acc: 0.8840 \n", "Epoch 339/1000\n", "500/500 [==============================] - 0s - loss: 0.0875 - acc: 0.8840 \n", "Epoch 340/1000\n", "500/500 [==============================] - 0s - loss: 0.0874 - acc: 0.8840 \n", "Epoch 341/1000\n", "500/500 [==============================] - 0s - loss: 0.0873 - acc: 0.8840 \n", "Epoch 342/1000\n", "500/500 [==============================] - 0s - loss: 0.0872 - acc: 0.8860 \n", "Epoch 343/1000\n", "500/500 [==============================] - 0s - loss: 0.0871 - acc: 0.8840 \n", "Epoch 344/1000\n", "500/500 [==============================] - 0s - loss: 0.0870 - acc: 0.8840 \n", "Epoch 345/1000\n", "500/500 [==============================] - 0s - loss: 0.0870 - acc: 0.8840 \n", "Epoch 346/1000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "500/500 [==============================] - 0s - loss: 0.0868 - acc: 0.8860 \n", "Epoch 347/1000\n", "500/500 [==============================] - 0s - loss: 0.0868 - acc: 0.8840 \n", "Epoch 348/1000\n", "500/500 [==============================] - 0s - loss: 0.0867 - acc: 0.8860 \n", "Epoch 349/1000\n", "500/500 [==============================] - 0s - loss: 0.0865 - acc: 0.8860 \n", "Epoch 350/1000\n", "500/500 [==============================] - 0s - loss: 0.0865 - acc: 0.8860 \n", "Epoch 351/1000\n", "500/500 [==============================] - 0s - loss: 0.0864 - acc: 0.8860 \n", "Epoch 352/1000\n", "500/500 [==============================] - 0s - loss: 0.0863 - acc: 0.8860 \n", "Epoch 353/1000\n", "500/500 [==============================] - 0s - loss: 0.0862 - acc: 0.8860 \n", "Epoch 354/1000\n", "500/500 [==============================] - 0s - loss: 0.0861 - acc: 0.8860 \n", "Epoch 355/1000\n", "500/500 [==============================] - 0s - loss: 0.0861 - acc: 0.8860 \n", "Epoch 356/1000\n", "500/500 [==============================] - 0s - loss: 0.0859 - acc: 0.8860 \n", "Epoch 357/1000\n", "500/500 [==============================] - 0s - loss: 0.0859 - acc: 0.8860 \n", "Epoch 358/1000\n", "500/500 [==============================] - 0s - loss: 0.0857 - acc: 0.8860 \n", "Epoch 359/1000\n", "500/500 [==============================] - 0s - loss: 0.0856 - acc: 0.8860 \n", "Epoch 360/1000\n", "500/500 [==============================] - 0s - loss: 0.0856 - acc: 0.8880 \n", "Epoch 361/1000\n", "500/500 [==============================] - 0s - loss: 0.0855 - acc: 0.8880 \n", "Epoch 362/1000\n", "500/500 [==============================] - 0s - loss: 0.0854 - acc: 0.8880 \n", "Epoch 363/1000\n", "500/500 [==============================] - 0s - loss: 0.0853 - acc: 0.8880 \n", "Epoch 364/1000\n", "500/500 [==============================] - 0s - loss: 0.0852 - acc: 0.8860 \n", "Epoch 365/1000\n", "500/500 [==============================] - 0s - loss: 0.0851 - acc: 0.8880 \n", "Epoch 366/1000\n", "500/500 [==============================] - 0s - loss: 0.0850 - acc: 0.8880 \n", "Epoch 367/1000\n", "500/500 [==============================] - 0s - loss: 0.0849 - acc: 0.8880 \n", "Epoch 368/1000\n", "500/500 [==============================] - 0s - loss: 0.0848 - acc: 0.8880 \n", "Epoch 369/1000\n", "500/500 [==============================] - 0s - loss: 0.0847 - acc: 0.8880 \n", "Epoch 370/1000\n", "500/500 [==============================] - 0s - loss: 0.0846 - acc: 0.8880 \n", "Epoch 371/1000\n", "500/500 [==============================] - 0s - loss: 0.0845 - acc: 0.8880 \n", "Epoch 372/1000\n", "500/500 [==============================] - 0s - loss: 0.0845 - acc: 0.8880 \n", "Epoch 373/1000\n", "500/500 [==============================] - 0s - loss: 0.0844 - acc: 0.8880 \n", "Epoch 374/1000\n", "500/500 [==============================] - 0s - loss: 0.0843 - acc: 0.8880 \n", "Epoch 375/1000\n", "500/500 [==============================] - 0s - loss: 0.0842 - acc: 0.8880 \n", "Epoch 376/1000\n", "500/500 [==============================] - 0s - loss: 0.0841 - acc: 0.8880 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[==============================] - 0s - loss: 0.0804 - acc: 0.8940 \n", "Epoch 417/1000\n", "500/500 [==============================] - 0s - loss: 0.0803 - acc: 0.8920 \n", "Epoch 418/1000\n", "500/500 [==============================] - 0s - loss: 0.0802 - acc: 0.8940 \n", "Epoch 419/1000\n", "500/500 [==============================] - 0s - loss: 0.0801 - acc: 0.8940 \n", "Epoch 420/1000\n", "500/500 [==============================] - 0s - loss: 0.0800 - acc: 0.8940 \n", "Epoch 421/1000\n", "500/500 [==============================] - 0s - loss: 0.0799 - acc: 0.8960 \n", "Epoch 422/1000\n", "500/500 [==============================] - 0s - loss: 0.0798 - acc: 0.8980 \n", "Epoch 423/1000\n", "500/500 [==============================] - 0s - loss: 0.0797 - acc: 0.8980 \n", "Epoch 424/1000\n", "500/500 [==============================] - 0s - loss: 0.0797 - acc: 0.8960 \n", "Epoch 425/1000\n", "500/500 [==============================] - 0s - loss: 0.0796 - acc: 0.8980 \n", "Epoch 426/1000\n", "500/500 [==============================] - 0s - loss: 0.0795 - acc: 0.8980 \n", "Epoch 427/1000\n", "500/500 [==============================] - 0s - loss: 0.0794 - acc: 0.8980 \n", "Epoch 428/1000\n", "500/500 [==============================] - 0s - loss: 0.0793 - acc: 0.8980 \n", "Epoch 429/1000\n", "500/500 [==============================] - 0s - loss: 0.0792 - acc: 0.8980 \n", "Epoch 430/1000\n", "500/500 [==============================] - 0s - loss: 0.0791 - acc: 0.8980 \n", "Epoch 431/1000\n", "500/500 [==============================] - 0s - loss: 0.0790 - acc: 0.8980 \n", "Epoch 432/1000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "500/500 [==============================] - 0s - loss: 0.0790 - acc: 0.8980 \n", "Epoch 433/1000\n", "500/500 [==============================] - 0s - loss: 0.0789 - acc: 0.8980 \n", "Epoch 434/1000\n", "500/500 [==============================] - 0s - loss: 0.0788 - acc: 0.8980 \n", "Epoch 435/1000\n", "500/500 [==============================] - 0s - loss: 0.0787 - acc: 0.8980 \n", "Epoch 436/1000\n", "500/500 [==============================] - 0s - loss: 0.0786 - acc: 0.8980 \n", "Epoch 437/1000\n", "500/500 [==============================] - 0s - loss: 0.0785 - acc: 0.8980 \n", "Epoch 438/1000\n", "500/500 [==============================] - 0s - loss: 0.0784 - acc: 0.8980 \n", "Epoch 439/1000\n", "500/500 [==============================] - 0s - loss: 0.0783 - acc: 0.8980 \n", "Epoch 440/1000\n", "500/500 [==============================] - 0s - loss: 0.0782 - acc: 0.8980 \n", "Epoch 441/1000\n", "500/500 [==============================] - 0s - loss: 0.0781 - acc: 0.8980 \n", "Epoch 442/1000\n", "500/500 [==============================] - 0s - loss: 0.0780 - acc: 0.8980 \n", "Epoch 443/1000\n", "500/500 [==============================] - 0s - loss: 0.0780 - acc: 0.8980 \n", "Epoch 444/1000\n", "500/500 [==============================] - 0s - loss: 0.0779 - acc: 0.8980 \n", "Epoch 445/1000\n", "500/500 [==============================] - 0s - loss: 0.0778 - acc: 0.9000 \n", "Epoch 446/1000\n", "500/500 [==============================] - 0s - loss: 0.0777 - acc: 0.9000 \n", "Epoch 447/1000\n", "500/500 [==============================] - 0s - loss: 0.0776 - acc: 0.9000 \n", "Epoch 448/1000\n", "500/500 [==============================] - 0s - loss: 0.0775 - acc: 0.9000 \n", "Epoch 449/1000\n", "500/500 [==============================] - 0s - loss: 0.0774 - acc: 0.9000 \n", "Epoch 450/1000\n", "500/500 [==============================] - 0s - loss: 0.0773 - acc: 0.9000 \n", "Epoch 451/1000\n", "500/500 [==============================] - 0s - loss: 0.0772 - acc: 0.9000 \n", "Epoch 452/1000\n", "500/500 [==============================] - 0s - loss: 0.0772 - acc: 0.9000 \n", "Epoch 453/1000\n", "500/500 [==============================] - 0s - loss: 0.0771 - acc: 0.9000 \n", "Epoch 454/1000\n", "500/500 [==============================] - 0s - loss: 0.0770 - acc: 0.9000 \n", "Epoch 455/1000\n", "500/500 [==============================] - 0s - loss: 0.0769 - acc: 0.9000 \n", "Epoch 456/1000\n", "500/500 [==============================] - 0s - loss: 0.0768 - acc: 0.9000 \n", "Epoch 457/1000\n", "500/500 [==============================] - 0s - loss: 0.0767 - acc: 0.9000 \n", "Epoch 458/1000\n", "500/500 [==============================] - 0s - loss: 0.0766 - acc: 0.9000 \n", "Epoch 459/1000\n", "500/500 [==============================] - 0s - loss: 0.0767 - acc: 0.9000 \n", "Epoch 460/1000\n", "500/500 [==============================] - 0s - loss: 0.0765 - acc: 0.9000 \n", "Epoch 461/1000\n", "500/500 [==============================] - 0s - loss: 0.0764 - acc: 0.9000 \n", "Epoch 462/1000\n", "500/500 [==============================] - 0s - loss: 0.0763 - acc: 0.9000 \n", "Epoch 463/1000\n", "500/500 [==============================] - 0s - loss: 0.0762 - acc: 0.9000 \n", "Epoch 464/1000\n", "500/500 [==============================] - 0s - loss: 0.0761 - acc: 0.9000 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acc: 0.9000 \n", "Epoch 475/1000\n", "500/500 [==============================] - 0s - loss: 0.0751 - acc: 0.9000 \n", "Epoch 476/1000\n", "500/500 [==============================] - 0s - loss: 0.0751 - acc: 0.9000 \n", "Epoch 477/1000\n", "500/500 [==============================] - 0s - loss: 0.0751 - acc: 0.9000 \n", "Epoch 478/1000\n", "500/500 [==============================] - 0s - loss: 0.0749 - acc: 0.9000 \n", "Epoch 479/1000\n", "500/500 [==============================] - 0s - loss: 0.0748 - acc: 0.9000 \n", "Epoch 480/1000\n", "500/500 [==============================] - 0s - loss: 0.0748 - acc: 0.9000 \n", "Epoch 481/1000\n", "500/500 [==============================] - 0s - loss: 0.0747 - acc: 0.9000 \n", "Epoch 482/1000\n", "500/500 [==============================] - 0s - loss: 0.0746 - acc: 0.9000 \n", "Epoch 483/1000\n", "500/500 [==============================] - 0s - loss: 0.0745 - acc: 0.9000 \n", "Epoch 484/1000\n", "500/500 [==============================] - 0s - loss: 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0s - loss: 0.0735 - acc: 0.9000 \n", "Epoch 495/1000\n", "500/500 [==============================] - 0s - loss: 0.0735 - acc: 0.9020 \n", "Epoch 496/1000\n", "500/500 [==============================] - 0s - loss: 0.0735 - acc: 0.9020 \n", "Epoch 497/1000\n", "500/500 [==============================] - 0s - loss: 0.0733 - acc: 0.9020 \n", "Epoch 498/1000\n", "500/500 [==============================] - 0s - loss: 0.0732 - acc: 0.9020 \n", "Epoch 499/1000\n", "500/500 [==============================] - 0s - loss: 0.0731 - acc: 0.9020 \n", "Epoch 500/1000\n", "500/500 [==============================] - 0s - loss: 0.0731 - acc: 0.9020 \n", "Epoch 501/1000\n", "500/500 [==============================] - 0s - loss: 0.0730 - acc: 0.9020 \n", "Epoch 502/1000\n", "500/500 [==============================] - 0s - loss: 0.0729 - acc: 0.9020 \n", "Epoch 503/1000\n", "500/500 [==============================] - 0s - loss: 0.0728 - acc: 0.9040 \n", "Epoch 504/1000\n", "500/500 [==============================] - 0s - loss: 0.0727 - acc: 0.9040 \n", "Epoch 505/1000\n", "500/500 [==============================] - 0s - loss: 0.0727 - acc: 0.9040 \n", "Epoch 506/1000\n", "500/500 [==============================] - 0s - loss: 0.0726 - acc: 0.9020 \n", "Epoch 507/1000\n", "500/500 [==============================] - 0s - loss: 0.0725 - acc: 0.9020 \n", "Epoch 508/1000\n", "500/500 [==============================] - 0s - loss: 0.0724 - acc: 0.9020 \n", "Epoch 509/1000\n", "500/500 [==============================] - ETA: 0s - loss: 0.0354 - acc: 0.968 - 0s - loss: 0.0723 - acc: 0.9040 \n", "Epoch 510/1000\n", "500/500 [==============================] - 0s - loss: 0.0723 - acc: 0.9040 \n", "Epoch 511/1000\n", "500/500 [==============================] - 0s - loss: 0.0722 - acc: 0.9040 \n", "Epoch 512/1000\n", "500/500 [==============================] - 0s - loss: 0.0721 - acc: 0.9040 \n", "Epoch 513/1000\n", "500/500 [==============================] - 0s - loss: 0.0720 - acc: 0.9040 \n", "Epoch 514/1000\n", "500/500 [==============================] - 0s - loss: 0.0719 - acc: 0.9040 \n", "Epoch 515/1000\n", "500/500 [==============================] - 0s - loss: 0.0718 - acc: 0.9040 \n", "Epoch 516/1000\n", "500/500 [==============================] - 0s - loss: 0.0718 - acc: 0.9040 \n", "Epoch 517/1000\n", "500/500 [==============================] - 0s - loss: 0.0717 - acc: 0.9040 \n", "Epoch 518/1000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "500/500 [==============================] - 0s - loss: 0.0716 - acc: 0.9040 \n", "Epoch 519/1000\n", "500/500 [==============================] - 0s - loss: 0.0715 - acc: 0.9040 \n", "Epoch 520/1000\n", "500/500 [==============================] - 0s - loss: 0.0714 - acc: 0.9040 \n", "Epoch 521/1000\n", "500/500 [==============================] - 0s - loss: 0.0714 - acc: 0.9040 \n", "Epoch 522/1000\n", "500/500 [==============================] - 0s - loss: 0.0713 - acc: 0.9040 \n", "Epoch 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0.0690 - acc: 0.9060 \n", "Epoch 553/1000\n", "500/500 [==============================] - 0s - loss: 0.0690 - acc: 0.9040 \n", "Epoch 554/1000\n", "500/500 [==============================] - 0s - loss: 0.0689 - acc: 0.9060 \n", "Epoch 555/1000\n", "500/500 [==============================] - 0s - loss: 0.0688 - acc: 0.9080 \n", "Epoch 556/1000\n", "500/500 [==============================] - 0s - loss: 0.0687 - acc: 0.9080 \n", "Epoch 557/1000\n", "500/500 [==============================] - 0s - loss: 0.0687 - acc: 0.9060 \n", "Epoch 558/1000\n", "500/500 [==============================] - 0s - loss: 0.0686 - acc: 0.9080 \n", "Epoch 559/1000\n", "500/500 [==============================] - 0s - loss: 0.0685 - acc: 0.9080 \n", "Epoch 560/1000\n", "500/500 [==============================] - 0s - loss: 0.0685 - acc: 0.9080 \n", "Epoch 561/1000\n", "500/500 [==============================] - 0s - loss: 0.0684 - acc: 0.9080 \n", "Epoch 562/1000\n", "500/500 [==============================] - 0s - loss: 0.0683 - acc: 0.9080 \n", "Epoch 563/1000\n", "500/500 [==============================] - 0s - loss: 0.0682 - acc: 0.9060 \n", "Epoch 564/1000\n", "500/500 [==============================] - 0s - loss: 0.0682 - acc: 0.9080 \n", "Epoch 565/1000\n", "500/500 [==============================] - 0s - loss: 0.0681 - acc: 0.9100 \n", "Epoch 566/1000\n", "500/500 [==============================] - 0s - loss: 0.0680 - acc: 0.9100 \n", "Epoch 567/1000\n", "500/500 [==============================] - 0s - loss: 0.0679 - acc: 0.9080 \n", "Epoch 568/1000\n", "500/500 [==============================] - 0s - loss: 0.0678 - acc: 0.9060 \n", "Epoch 569/1000\n", "500/500 [==============================] - 0s - loss: 0.0678 - acc: 0.9080 \n", "Epoch 570/1000\n", "500/500 [==============================] - 0s - loss: 0.0677 - acc: 0.9080 \n", "Epoch 571/1000\n", "500/500 [==============================] - 0s - loss: 0.0676 - acc: 0.9080 \n", "Epoch 572/1000\n", "500/500 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"500/500 [==============================] - 0s - loss: 0.0668 - acc: 0.9100 \n", "Epoch 583/1000\n", "500/500 [==============================] - 0s - loss: 0.0668 - acc: 0.9140 \n", "Epoch 584/1000\n", "500/500 [==============================] - 0s - loss: 0.0667 - acc: 0.9120 \n", "Epoch 585/1000\n", "500/500 [==============================] - 0s - loss: 0.0666 - acc: 0.9100 \n", "Epoch 586/1000\n", "500/500 [==============================] - 0s - loss: 0.0666 - acc: 0.9140 \n", "Epoch 587/1000\n", "500/500 [==============================] - 0s - loss: 0.0665 - acc: 0.9140 \n", "Epoch 588/1000\n", "500/500 [==============================] - 0s - loss: 0.0664 - acc: 0.9140 \n", "Epoch 589/1000\n", "500/500 [==============================] - 0s - loss: 0.0663 - acc: 0.9140 \n", "Epoch 590/1000\n", "500/500 [==============================] - 0s - loss: 0.0663 - acc: 0.9100 \n", "Epoch 591/1000\n", "500/500 [==============================] - 0s - loss: 0.0662 - acc: 0.9100 \n", "Epoch 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690/1000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "500/500 [==============================] - 0s - loss: 0.0599 - acc: 0.9320 \n", "Epoch 691/1000\n", "500/500 [==============================] - 0s - loss: 0.0600 - acc: 0.9320 \n", "Epoch 692/1000\n", "500/500 [==============================] - 0s - loss: 0.0598 - acc: 0.9320 \n", "Epoch 693/1000\n", "500/500 [==============================] - 0s - loss: 0.0598 - acc: 0.9320 \n", "Epoch 694/1000\n", "500/500 [==============================] - 0s - loss: 0.0597 - acc: 0.9300 \n", "Epoch 695/1000\n", "500/500 [==============================] - 0s - loss: 0.0597 - acc: 0.9300 \n", "Epoch 696/1000\n", "500/500 [==============================] - 0s - loss: 0.0596 - acc: 0.9320 \n", "Epoch 697/1000\n", "500/500 [==============================] - 0s - loss: 0.0596 - acc: 0.9320 \n", "Epoch 698/1000\n", "500/500 [==============================] - 0s - loss: 0.0595 - acc: 0.9320 \n", "Epoch 699/1000\n", "500/500 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\n", "Epoch 993/1000\n", "500/500 [==============================] - 0s - loss: 0.0468 - acc: 0.9540 \n", "Epoch 994/1000\n", "500/500 [==============================] - 0s - loss: 0.0467 - acc: 0.9540 \n", "Epoch 995/1000\n", "500/500 [==============================] - 0s - loss: 0.0466 - acc: 0.9520 \n", "Epoch 996/1000\n", "500/500 [==============================] - 0s - loss: 0.0466 - acc: 0.9520 \n", "Epoch 997/1000\n", "500/500 [==============================] - 0s - loss: 0.0465 - acc: 0.9520 \n", "Epoch 998/1000\n", "500/500 [==============================] - 0s - loss: 0.0465 - acc: 0.9540 \n", "Epoch 999/1000\n", "500/500 [==============================] - 0s - loss: 0.0465 - acc: 0.9520 \n", "Epoch 1000/1000\n", "500/500 [==============================] - 0s - loss: 0.0465 - acc: 0.9520 \n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "modelann.compile(optimizer='adam', \n", " loss='mse', \n", " metrics=['accuracy'])\n", "modelann.fit(x=X,y=y, epochs=1000)\n", "#x = modelann.evaluate(X, y)\n", "#print('\\n',x)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2O/ZH7bpJvZFCWAiMR+X7n4dm33ZlmtT9sKDJa6s+/bbJkhphQ4sxbtMw0N0e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YAAAg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\nMc1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", "h = 0.01\n", "# Generate a grid of points with distance h between them\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), \n", " np.arange(y_min, y_max, h))\n", "# Predict the function value for the whole gid\n", "Z= modelann.predict(np.c_[xx.ravel(),yy.ravel()])\n", "Z[Z>=0.5]=1\n", "Z[Z<0.5]=0\n", "\n", "Z = Z.reshape(xx.shape)\n", "# Plot the contour and training examples\n", "plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n", "plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.BuGn)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }