From e0fec7ab02578aa9962c25b26901a84282ac325f Mon Sep 17 00:00:00 2001 From: ArtemShaprio Date: Tue, 21 May 2019 10:45:51 +0300 Subject: [PATCH 1/3] Task cnn --- cnn/CNN_application.ipynb | 293 ++++++++++++++++++-------- cnn/CNN_step_by_step.ipynb | 420 ++++++++++++++++++++++--------------- 2 files changed, 463 insertions(+), 250 deletions(-) diff --git a/cnn/CNN_application.ipynb b/cnn/CNN_application.ipynb index 5e9d105..0a806b0 100644 --- a/cnn/CNN_application.ipynb +++ b/cnn/CNN_application.ipynb @@ -31,10 +31,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "import math\n", @@ -52,6 +50,27 @@ "np.random.seed(1)" ] }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.13.1'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__ \n", + "# NEEDED '1.2.1' for proper working ?????????????????????" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -61,10 +80,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "# Loading the data (signs)\n", @@ -84,11 +101,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y = 2\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Example of a picture\n", "index = 6\n", @@ -107,11 +142,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of training examples = 1080\n", + "number of test examples = 120\n", + "X_train shape: (1080, 64, 64, 3)\n", + "Y_train shape: (1080, 6)\n", + "X_test shape: (120, 64, 64, 3)\n", + "Y_test shape: (120, 6)\n" + ] + } + ], "source": [ "X_train = X_train_orig/255.\n", "X_test = X_test_orig/255.\n", @@ -141,10 +187,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: create_placeholders\n", @@ -165,8 +209,8 @@ " \"\"\"\n", "\n", " ### START CODE HERE ### (≈2 lines)\n", - " X = None\n", - " Y = None\n", + " X = tf.placeholder(tf.float32, shape=(None, n_H0, n_W0, n_C0))\n", + " Y = tf.placeholder(tf.float32, shape=(None, n_y))\n", " ### END CODE HERE ###\n", " \n", " return X, Y" @@ -174,11 +218,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X = Tensor(\"Placeholder:0\", shape=(?, 64, 64, 3), dtype=float32)\n", + "Y = Tensor(\"Placeholder_1:0\", shape=(?, 6), dtype=float32)\n" + ] + } + ], "source": [ "X, Y = create_placeholders(64, 64, 3, 6)\n", "print (\"X = \" + str(X))\n", @@ -224,10 +275,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: initialize_parameters\n", @@ -244,8 +293,8 @@ " tf.set_random_seed(1) # so that your \"random\" numbers match ours\n", " \n", " ### START CODE HERE ### (approx. 2 lines of code)\n", - " W1 = None\n", - " W2 = None\n", + " W1 = tf.get_variable(\"W1\", [4, 4, 3, 8], initializer = tf.contrib.layers.xavier_initializer(seed = 0))\n", + " W2 = tf.get_variable(\"W2\", [2, 2, 8, 16], initializer = tf.contrib.layers.xavier_initializer(seed = 0))\n", " ### END CODE HERE ###\n", "\n", " parameters = {\"W1\": W1,\n", @@ -256,11 +305,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [ 0.00131723 0.1417614 -0.04434952 0.09197326 0.14984085 -0.03514394\n", + " -0.06847463 0.05245192]\n", + "W2 = [-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058\n", + " -0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228\n", + " -0.22779644 -0.1601823 -0.16117483 -0.10286498]\n" + ] + } + ], "source": [ "tf.reset_default_graph()\n", "with tf.Session() as sess_test:\n", @@ -341,10 +400,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: forward_propagation\n", @@ -369,22 +426,22 @@ " \n", " ### START CODE HERE ###\n", " # CONV2D: stride of 1, padding 'SAME'\n", - " Z1 = None\n", + " Z1 = tf.nn.conv2d(X, W1, strides = [1, 1, 1, 1], padding='SAME')\n", " # RELU\n", - " A1 = None\n", + " A1 = tf.nn.relu(Z1)\n", " # MAXPOOL: window 8x8, sride 8, padding 'SAME'\n", - " P1 = None\n", + " P1 = tf.nn.max_pool(A1, ksize=[1, 8, 8, 1], strides = [1, 8, 8, 1], padding = 'SAME')\n", " # CONV2D: filters W2, stride 1, padding 'SAME'\n", - " Z2 = None\n", + " Z2 = tf.nn.conv2d(P1, W2, strides = [1, 1, 1, 1], padding='SAME')\n", " # RELU\n", - " A2 = None\n", + " A2 = tf.nn.relu(Z2)\n", " # MAXPOOL: window 4x4, stride 4, padding 'SAME'\n", - " P2 = None\n", + " P2 = tf.nn.max_pool(A2, ksize=[1, 4, 4, 1], strides = [1, 4, 4, 1], padding = 'SAME')\n", " # FLATTEN\n", - " P2 = None\n", + " P2 = tf.contrib.layers.flatten(P2)\n", " # FULLY-CONNECTED without non-linear activation function (not not call softmax).\n", " # 6 neurons in output layer. Hint: one of the arguments should be \"activation_fn=None\" \n", - " Z3 = None\n", + " Z3 = tf.contrib.layers.fully_connected(P2, 6, activation_fn=None)\n", " ### END CODE HERE ###\n", "\n", " return Z3" @@ -392,11 +449,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /mnt/c/Users/Allah/Documents/courses/main/ml_course/lib/python3.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py:1624: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.flatten instead.\n", + "Z3 = [[ 1.4416984 -0.24909666 5.450499 -0.2618962 -0.20669907 1.3654671 ]\n", + " [ 1.4070846 -0.02573211 5.08928 -0.48669922 -0.40940708 1.2624859 ]]\n" + ] + } + ], "source": [ "tf.reset_default_graph()\n", "\n", @@ -444,10 +511,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: compute_cost \n", @@ -465,7 +530,7 @@ " \"\"\"\n", " \n", " ### START CODE HERE ### (1 line of code)\n", - " cost = None\n", + " cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = Z3, labels = Y))\n", " ### END CODE HERE ###\n", " \n", " return cost" @@ -473,11 +538,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :16: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "\n", + "Future major versions of TensorFlow will allow gradients to flow\n", + "into the labels input on backprop by default.\n", + "\n", + "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n", + "\n", + "cost = 4.6648693\n" + ] + } + ], "source": [ "tf.reset_default_graph()\n", "\n", @@ -535,10 +614,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: model\n", @@ -574,27 +651,27 @@ " \n", " # Create Placeholders of the correct shape\n", " ### START CODE HERE ### (1 line)\n", - " X, Y = None\n", + " X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y)\n", " ### END CODE HERE ###\n", "\n", " # Initialize parameters\n", " ### START CODE HERE ### (1 line)\n", - " parameters = None\n", + " parameters = initialize_parameters()\n", " ### END CODE HERE ###\n", " \n", " # Forward propagation: Build the forward propagation in the tensorflow graph\n", " ### START CODE HERE ### (1 line)\n", - " Z3 = None\n", + " Z3 = forward_propagation(X, parameters)\n", " ### END CODE HERE ###\n", " \n", " # Cost function: Add cost function to tensorflow graph\n", " ### START CODE HERE ### (1 line)\n", - " cost = None\n", + " cost = compute_cost(Z3, Y)\n", " ### END CODE HERE ###\n", " \n", " # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.\n", " ### START CODE HERE ### (1 line)\n", - " optimizer = None\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", " ### END CODE HERE ###\n", " \n", " # Initialize all the variables globally\n", @@ -621,7 +698,7 @@ " # IMPORTANT: The line that runs the graph on a minibatch.\n", " # Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).\n", " ### START CODE HERE ### (1 line)\n", - " _ , temp_cost = None\n", + " _ , temp_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})\n", " ### END CODE HERE ###\n", " \n", " minibatch_cost += temp_cost / num_minibatches\n", @@ -665,11 +742,57 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after epoch 0: 1.921332\n", + "Cost after epoch 5: 1.904156\n", + "Cost after epoch 10: 1.904309\n", + "Cost after epoch 15: 1.904477\n", + "Cost after epoch 20: 1.901876\n", + "Cost after epoch 25: 1.784077\n", + "Cost after epoch 30: 1.681052\n", + "Cost after epoch 35: 1.618207\n", + "Cost after epoch 40: 1.597972\n", + "Cost after epoch 45: 1.566707\n", + "Cost after epoch 50: 1.554486\n", + "Cost after epoch 55: 1.502187\n", + "Cost after epoch 60: 1.461035\n", + "Cost after epoch 65: 1.304477\n", + "Cost after epoch 70: 1.201501\n", + "Cost after epoch 75: 1.144230\n", + "Cost after epoch 80: 1.098368\n", + "Cost after epoch 85: 1.077411\n", + "Cost after epoch 90: 1.043173\n", + "Cost after epoch 95: 1.022620\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(\"Mean_1:0\", shape=(), dtype=float32)\n", + "Train Accuracy: 0.6638889\n", + "Test Accuracy: 0.55\n" + ] + } + ], "source": [ "_, _, parameters = model(X_train, Y_train, X_test, Y_test)" ] @@ -733,9 +856,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "fname = \"images/thumbs_up.jpg\"\n", @@ -766,7 +887,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.2" } }, "nbformat": 4, diff --git a/cnn/CNN_step_by_step.ipynb b/cnn/CNN_step_by_step.ipynb index 944d43f..56619b7 100644 --- a/cnn/CNN_step_by_step.ipynb +++ b/cnn/CNN_step_by_step.ipynb @@ -42,9 +42,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -126,10 +124,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: zero_pad\n", @@ -148,7 +144,7 @@ " \"\"\"\n", " \n", " ### START CODE HERE ### (≈ 1 line)\n", - " X_pad = None\n", + " X_pad = np.pad(X, ((0,0), (pad,pad), (pad,pad), (0,0)), 'constant')\n", " ### END CODE HERE ###\n", " \n", " return X_pad" @@ -156,11 +152,50 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x.shape = (4, 3, 3, 2)\n", + "x_pad.shape = (4, 7, 7, 2)\n", + "x[1,1] = [[ 0.90085595 -0.68372786]\n", + " [-0.12289023 -0.93576943]\n", + " [-0.26788808 0.53035547]]\n", + "x_pad[1,1] = [[0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "np.random.seed(1)\n", "x = np.random.randn(4, 3, 3, 2)\n", @@ -252,10 +287,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: conv_single_step\n", @@ -276,11 +309,11 @@ "\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " # Element-wise product between a_slice and W. Do not add the bias yet.\n", - " s = None\n", + " s = a_slice_prev * W\n", " # Sum over all entries of the volume s.\n", - " Z = None\n", + " Z = np.sum(s)\n", " # Add bias b to Z. Cast b to a float() so that Z results in a scalar value.\n", - " Z = None\n", + " Z += np.float_(b)\n", " ### END CODE HERE ###\n", "\n", " return Z" @@ -288,11 +321,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z = -6.999089450680221\n" + ] + } + ], "source": [ "np.random.seed(1)\n", "a_slice_prev = np.random.randn(4, 4, 3)\n", @@ -361,14 +400,11 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: conv_forward\n", - "\n", "def conv_forward(A_prev, W, b, hparameters):\n", " \"\"\"\n", " Implements the forward propagation for a convolution function\n", @@ -386,43 +422,42 @@ " \n", " ### START CODE HERE ###\n", " # Retrieve dimensions from A_prev's shape (≈1 line) \n", - " (m, n_H_prev, n_W_prev, n_C_prev) = None\n", + " (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n", " \n", " # Retrieve dimensions from W's shape (≈1 line)\n", - " (f, f, n_C_prev, n_C) = None\n", + " (f, f, n_C_prev, n_C) = W.shape\n", " \n", " # Retrieve information from \"hparameters\" (≈2 lines)\n", - " stride = None\n", - " pad = None\n", + " stride = hparameters['stride']\n", + " pad = hparameters['pad']\n", " \n", " # Compute the dimensions of the CONV output volume using the formula given above. Hint: use int() to floor. (≈2 lines)\n", - " n_H = None\n", - " n_W = None\n", + " n_H = int((n_H_prev - f + 2 * pad)/stride) + 1\n", + " n_W = int((n_W_prev - f + 2 * pad)/stride) + 1\n", " \n", " # Initialize the output volume Z with zeros. (≈1 line)\n", - " Z = None\n", + " Z = np.zeros((m, n_H, n_W, n_C))\n", " \n", " # Create A_prev_pad by padding A_prev\n", - " A_prev_pad = None\n", + " A_prev_pad = zero_pad(A_prev, pad)\n", " \n", - " for i in range(None): # loop over the batch of training examples\n", - " a_prev_pad = None # Select ith training example's padded activation\n", - " for h in range(None): # loop over vertical axis of the output volume\n", - " for w in range(None): # loop over horizontal axis of the output volume\n", - " for c in range(None): # loop over channels (= #filters) of the output volume\n", - " \n", + " for i in range(m): # loop over the batch of training examples\n", + " a_prev_pad = A_prev_pad[i] # Select ith training example's padded activation\n", + " for h in range(0, n_H): # loop over vertical axis of the output volume\n", + " for w in range(0, n_W): # loop over horizontal axis of the output volume\n", + " for c in range(n_C): # loop over channels (= #filters) of the output volume\n", " # Find the corners of the current \"slice\" (≈4 lines)\n", - " vert_start = None\n", - " vert_end = None\n", - " horiz_start = None\n", - " horiz_end = None\n", - " \n", + " vert_start = h * stride\n", + " vert_end = h * stride + f\n", + " horiz_start = w * stride\n", + " horiz_end = w * stride + f\n", + " \n", " # Use the corners to define the (3D) slice of a_prev_pad (See Hint above the cell). (≈1 line)\n", - " a_slice_prev = None\n", - " \n", + " a_slice_prev = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end,:]\n", + " \n", " # Convolve the (3D) slice with the correct filter W and bias b, to get back one output neuron. (≈1 line)\n", - " Z[i, h, w, c] = None\n", - " \n", + " Z[i, h, w, c] = conv_single_step(a_slice_prev, W[:,:,:,c], b[:,:,:,c]) \n", + " \n", " ### END CODE HERE ###\n", " \n", " # Making sure your output shape is correct\n", @@ -436,11 +471,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z's mean = 0.048995203528855794\n", + "Z[3,2,1] = [-0.61490741 -6.7439236 -2.55153897 1.75698377 3.56208902 0.53036437\n", + " 5.18531798 8.75898442]\n", + "cache_conv[0][1][2][3] = [-0.20075807 0.18656139 0.41005165]\n" + ] + } + ], "source": [ "np.random.seed(1)\n", "A_prev = np.random.randn(10,4,4,3)\n", @@ -545,10 +589,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: pool_forward\n", @@ -571,7 +613,7 @@ " (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n", " \n", " # Retrieve hyperparameters from \"hparameters\"\n", - " f = hparameters[\"f\"]\n", + " f = hparameters[\"f\"]\n", " stride = hparameters[\"stride\"]\n", " \n", " # Define the dimensions of the output\n", @@ -581,27 +623,27 @@ " \n", " # Initialize output matrix A\n", " A = np.zeros((m, n_H, n_W, n_C)) \n", - " \n", + "\n", " ### START CODE HERE ###\n", - " for i in range(None): # loop over the training examples\n", - " for h in range(None): # loop on the vertical axis of the output volume\n", - " for w in range(None): # loop on the horizontal axis of the output volume\n", - " for c in range (None): # loop over the channels of the output volume\n", + " for i in range(m): # loop over the training examples\n", + " for h in range(n_H): # loop on the vertical axis of the output volume\n", + " for w in range(n_W): # loop on the horizontal axis of the output volume\n", + " for c in range (n_C): # loop over the channels of the output volume\n", " \n", " # Find the corners of the current \"slice\" (≈4 lines)\n", - " vert_start = None\n", - " vert_end = None\n", - " horiz_start = None\n", - " horiz_end = None\n", + " vert_start = h * stride\n", + " vert_end = h * stride + f\n", + " horiz_start = w * stride\n", + " horiz_end = w * stride + f\n", " \n", " # Use the corners to define the current slice on the ith training example of A_prev, channel c. (≈1 line)\n", - " a_prev_slice = None\n", + " a_prev_slice = A_prev[i][vert_start:vert_end, horiz_start:horiz_end,c]\n", " \n", " # Compute the pooling operation on the slice. Use an if statment to differentiate the modes. Use np.max/np.mean.\n", " if mode == \"max\":\n", - " A[i, h, w, c] = None\n", + " A[i, h, w, c] = np.max(a_prev_slice)\n", " elif mode == \"average\":\n", - " A[i, h, w, c] = None\n", + " A[i, h, w, c] = np.mean(a_prev_slice)\n", " \n", " ### END CODE HERE ###\n", " \n", @@ -616,11 +658,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mode = max\n", + "A = [[[[1.74481176 0.86540763 1.13376944]]]\n", + "\n", + "\n", + " [[[1.13162939 1.51981682 2.18557541]]]]\n", + "\n", + "mode = average\n", + "A = [[[[ 0.02105773 -0.20328806 -0.40389855]]]\n", + "\n", + "\n", + " [[[-0.22154621 0.51716526 0.48155844]]]]\n" + ] + } + ], "source": [ "np.random.seed(1)\n", "A_prev = np.random.randn(2, 4, 4, 3)\n", @@ -735,10 +793,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "def conv_backward(dZ, cache):\n", @@ -760,56 +816,56 @@ " \n", " ### START CODE HERE ###\n", " # Retrieve information from \"cache\"\n", - " (A_prev, W, b, hparameters) = None\n", + " (A_prev, W, b, hparameters) = cache\n", " \n", " # Retrieve dimensions from A_prev's shape\n", - " (m, n_H_prev, n_W_prev, n_C_prev) = None\n", + " (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n", " \n", " # Retrieve dimensions from W's shape\n", - " (f, f, n_C_prev, n_C) = None\n", + " (f, f, n_C_prev, n_C) = W.shape\n", " \n", " # Retrieve information from \"hparameters\"\n", - " stride = None\n", - " pad = None\n", + " stride = hparameters['stride']\n", + " pad = hparameters['pad']\n", " \n", " # Retrieve dimensions from dZ's shape\n", - " (m, n_H, n_W, n_C) = None\n", + " (m, n_H, n_W, n_C) = dZ.shape\n", " \n", " # Initialize dA_prev, dW, db with the correct shapes\n", - " dA_prev = None \n", - " dW = None\n", - " db = None\n", + " dA_prev = np.zeros((m, n_H_prev, n_W_prev, n_C_prev)) \n", + " dW = np.zeros((f, f, n_C_prev, n_C))\n", + " db = np.zeros((1, 1, 1, n_C))\n", "\n", " # Pad A_prev and dA_prev\n", - " A_prev_pad = None\n", - " dA_prev_pad = None\n", + " A_prev_pad = zero_pad(A_prev, pad)\n", + " dA_prev_pad = zero_pad(dA_prev, pad) \n", " \n", - " for i in range(None): # loop over the training examples\n", + " for i in range(m): # loop over the training examples\n", " \n", " # select ith training example from A_prev_pad and dA_prev_pad\n", - " a_prev_pad = None\n", - " da_prev_pad = None\n", + " a_prev_pad = A_prev_pad[i]\n", + " da_prev_pad = dA_prev_pad[i]\n", " \n", - " for h in range(None): # loop over vertical axis of the output volume\n", - " for w in range(None): # loop over horizontal axis of the output volume\n", - " for c in range(None): # loop over the channels of the output volume\n", + " for h in range(n_H): # loop over vertical axis of the output volume\n", + " for w in range(n_W): # loop over horizontal axis of the output volume\n", + " for c in range(n_C): # loop over the channels of the output volume\n", " \n", " # Find the corners of the current \"slice\"\n", - " vert_start = None\n", - " vert_end = None\n", - " horiz_start = None\n", - " horiz_end = None\n", + " vert_start = h * stride\n", + " vert_end = h * stride + f\n", + " horiz_start = w * stride\n", + " horiz_end = w * stride + f\n", " \n", " # Use the corners to define the slice from a_prev_pad\n", - " a_slice = None\n", + " a_slice = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end,:]\n", "\n", " # Update gradients for the window and the filter's parameters using the code formulas given above\n", - " da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] += None\n", - " dW[:,:,:,c] += None\n", - " db[:,:,:,c] += None\n", + " da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] += W[:,:,:,c] * dZ[i, h, w, c]\n", + " dW[:,:,:,c] += a_slice * dZ[i, h, w, c]\n", + " db[:,:,:,c] += dZ[i, h, w, c]\n", " \n", " # Set the ith training example's dA_prev to the unpaded da_prev_pad (Hint: use X[pad:-pad, pad:-pad, :])\n", - " dA_prev[i, :, :, :] = None\n", + " dA_prev[i, :, :, :] = da_prev_pad[pad:-pad, pad:-pad, :]\n", " ### END CODE HERE ###\n", " \n", " # Making sure your output shape is correct\n", @@ -820,12 +876,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { - "collapsed": true, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dA_mean = 1.4524377775388075\n", + "dW_mean = 1.7269914583139097\n", + "db_mean = 7.839232564616838\n" + ] + } + ], "source": [ "np.random.seed(1)\n", "dA, dW, db = conv_backward(Z, cache_conv)\n", @@ -903,10 +968,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "def create_mask_from_window(x):\n", @@ -921,7 +984,7 @@ " \"\"\"\n", " \n", " ### START CODE HERE ### (≈1 line)\n", - " mask = None\n", + " mask = (x == np.max(x))\n", " ### END CODE HERE ###\n", " \n", " return mask" @@ -929,12 +992,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { - "collapsed": true, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = [[ 1.62434536 -0.61175641 -0.52817175]\n", + " [-1.07296862 0.86540763 -2.3015387 ]]\n", + "mask = [[ True False False]\n", + " [False False False]]\n" + ] + } + ], "source": [ "np.random.seed(1)\n", "x = np.random.randn(2,3)\n", @@ -1008,10 +1081,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "def distribute_value(dz, shape):\n", @@ -1028,13 +1099,13 @@ " \n", " ### START CODE HERE ###\n", " # Retrieve dimensions from shape (≈1 line)\n", - " (n_H, n_W) = None\n", + " (n_H, n_W) = shape\n", " \n", " # Compute the value to distribute on the matrix (≈1 line)\n", - " average = None\n", + " average = dz/(n_H * n_W)\n", " \n", " # Create a matrix where every entry is the \"average\" value (≈1 line)\n", - " a = None\n", + " a = np.ones(shape) * average\n", " ### END CODE HERE ###\n", " \n", " return a" @@ -1042,11 +1113,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "distributed value = [[0.5 0.5]\n", + " [0.5 0.5]]\n" + ] + } + ], "source": [ "a = distribute_value(2, (2,2))\n", "print('distributed value =', a)" @@ -1085,10 +1163,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "def pool_backward(dA, cache, mode = \"max\"):\n", @@ -1107,52 +1183,52 @@ " ### START CODE HERE ###\n", " \n", " # Retrieve information from cache (≈1 line)\n", - " (A_prev, hparameters) = None\n", + " (A_prev, hparameters) = cache\n", " \n", " # Retrieve hyperparameters from \"hparameters\" (≈2 lines)\n", - " stride = None\n", - " f = None\n", + " stride = hparameters['stride']\n", + " f = hparameters['f']\n", " \n", " # Retrieve dimensions from A_prev's shape and dA's shape (≈2 lines)\n", - " m, n_H_prev, n_W_prev, n_C_prev = None\n", - " m, n_H, n_W, n_C = None\n", + " m, n_H_prev, n_W_prev, n_C_prev = A_prev.shape\n", + " m, n_H, n_W, n_C = dA.shape\n", " \n", " # Initialize dA_prev with zeros (≈1 line)\n", - " dA_prev = None\n", + " dA_prev = np.zeros(A_prev.shape)\n", " \n", - " for i in range(None): # loop over the training examples\n", + " for i in range(m): # loop over the training examples\n", " \n", " # select training example from A_prev (≈1 line)\n", - " a_prev = None\n", + " a_prev = A_prev[i]\n", " \n", - " for h in range(None): # loop on the vertical axis\n", - " for w in range(None): # loop on the horizontal axis\n", - " for c in range(None): # loop over the channels (depth)\n", + " for h in range(n_H): # loop on the vertical axis\n", + " for w in range(n_W): # loop on the horizontal axis\n", + " for c in range(n_C): # loop over the channels (depth)\n", " \n", " # Find the corners of the current \"slice\" (≈4 lines)\n", - " vert_start = None\n", - " vert_end = None\n", - " horiz_start = None\n", - " horiz_end = None\n", + " vert_start = h * stride\n", + " vert_end = h * stride + f\n", + " horiz_start = w * stride\n", + " horiz_end = w * stride + f\n", " \n", " # Compute the backward propagation in both modes.\n", " if mode == \"max\":\n", " \n", " # Use the corners and \"c\" to define the current slice from a_prev (≈1 line)\n", - " a_prev_slice = None\n", + " a_prev_slice = a_prev[vert_start:vert_end, horiz_start:horiz_end, c]\n", " # Create the mask from a_prev_slice (≈1 line)\n", - " mask = None\n", + " mask = create_mask_from_window(a_prev_slice)\n", " # Set dA_prev to be dA_prev + (the mask multiplied by the correct entry of dA) (≈1 line)\n", - " dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += None\n", + " dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += mask*dA[i, h, w, c]\n", " \n", " elif mode == \"average\":\n", " \n", " # Get the value a from dA (≈1 line)\n", - " da = None\n", + " da = dA[i, h, w, c]\n", " # Define the shape of the filter as fxf (≈1 line)\n", - " shape = None\n", + " shape = (f, f)\n", " # Distribute it to get the correct slice of dA_prev. i.e. Add the distributed value of da. (≈1 line)\n", - " dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += None\n", + " dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += distribute_value(da, shape)\n", " \n", " ### END CODE ###\n", " \n", @@ -1164,11 +1240,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mode = max\n", + "mean of dA = 0.14571390272918056\n", + "dA_prev[1,1] = [[ 0. 0. ]\n", + " [ 5.05844394 -1.68282702]\n", + " [ 0. 0. ]]\n", + "\n", + "mode = average\n", + "mean of dA = 0.14571390272918056\n", + "dA_prev[1,1] = [[ 0.08485462 0.2787552 ]\n", + " [ 1.26461098 -0.25749373]\n", + " [ 1.17975636 -0.53624893]]\n" + ] + } + ], "source": [ "np.random.seed(1)\n", "A_prev = np.random.randn(5, 5, 3, 2)\n", @@ -1279,7 +1371,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.2" } }, "nbformat": 4, From 097c360a419a49f19ca187fa67a70f4f9bfb2279 Mon Sep 17 00:00:00 2001 From: ArtemShaprio Date: Tue, 21 May 2019 11:20:34 +0300 Subject: [PATCH 2/3] Degrade version of Python to 3.5, version of TF to 1.2.0rc --- cnn/CNN_application.ipynb | 97 +++++++++++++++++---------------------- 1 file changed, 43 insertions(+), 54 deletions(-) diff --git a/cnn/CNN_application.ipynb b/cnn/CNN_application.ipynb index 0a806b0..b299574 100644 --- a/cnn/CNN_application.ipynb +++ b/cnn/CNN_application.ipynb @@ -52,16 +52,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1.13.1'" + "'1.2.0-rc0'" ] }, - "execution_count": 17, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -400,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -449,18 +449,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From /mnt/c/Users/Allah/Documents/courses/main/ml_course/lib/python3.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py:1624: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use keras.layers.flatten instead.\n", - "Z3 = [[ 1.4416984 -0.24909666 5.450499 -0.2618962 -0.20669907 1.3654671 ]\n", - " [ 1.4070846 -0.02573211 5.08928 -0.48669922 -0.40940708 1.2624859 ]]\n" + "Z3 = [[-0.44670227 -1.5720876 -1.5304923 -2.3101304 -1.2910438 0.46852064]\n", + " [-0.17601591 -1.5797201 -1.4737016 -2.616721 -1.0081065 0.5747785 ]]\n" ] } ], @@ -511,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -538,22 +535,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From :16: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "\n", - "Future major versions of TensorFlow will allow gradients to flow\n", - "into the labels input on backprop by default.\n", - "\n", - "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n", - "\n", - "cost = 4.6648693\n" + "cost = 2.9103396\n" ] } ], @@ -614,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -742,38 +731,38 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Cost after epoch 0: 1.921332\n", - "Cost after epoch 5: 1.904156\n", - "Cost after epoch 10: 1.904309\n", - "Cost after epoch 15: 1.904477\n", - "Cost after epoch 20: 1.901876\n", - "Cost after epoch 25: 1.784077\n", - "Cost after epoch 30: 1.681052\n", - "Cost after epoch 35: 1.618207\n", - "Cost after epoch 40: 1.597972\n", - "Cost after epoch 45: 1.566707\n", - "Cost after epoch 50: 1.554486\n", - "Cost after epoch 55: 1.502187\n", - "Cost after epoch 60: 1.461035\n", - "Cost after epoch 65: 1.304477\n", - "Cost after epoch 70: 1.201501\n", - "Cost after epoch 75: 1.144230\n", - "Cost after epoch 80: 1.098368\n", - "Cost after epoch 85: 1.077411\n", - "Cost after epoch 90: 1.043173\n", - "Cost after epoch 95: 1.022620\n" + "Cost after epoch 0: 1.917929\n", + "Cost after epoch 5: 1.506757\n", + "Cost after epoch 10: 0.955359\n", + "Cost after epoch 15: 0.845802\n", + "Cost after epoch 20: 0.701174\n", + "Cost after epoch 25: 0.571977\n", + "Cost after epoch 30: 0.518435\n", + "Cost after epoch 35: 0.495806\n", + "Cost after epoch 40: 0.429827\n", + "Cost after epoch 45: 0.407291\n", + "Cost after epoch 50: 0.366394\n", + "Cost after epoch 55: 0.376922\n", + "Cost after epoch 60: 0.299491\n", + "Cost after epoch 65: 0.338870\n", + "Cost after epoch 70: 0.316400\n", + "Cost after epoch 75: 0.310413\n", + "Cost after epoch 80: 0.249549\n", + "Cost after epoch 85: 0.243457\n", + "Cost after epoch 90: 0.200031\n", + "Cost after epoch 95: 0.175452\n" ] }, { "data": { - "image/png": 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gssqxZWcZrZIiJCaEcM7x0fwCHhi/iPOOzCEpEg64apH4o310CUw4ZGSmtiAxwftnaGb8/NTeFOwo5emJy4MtTiROKRSkURnaLYPje2Xx8MdL2F5S80FpEYkdhYI0Ojed0putO8u5Y9xcVmwqDrockbiiYwrS6AzMSeeSobk8+/lKXv5yNX06pHF0j0zyMluSl5FCv46tyEhtEXSZIs1SzE5JNbMngTOBAufcgFrajAT+AkSAjc654w+0XJ2SGj9Wbd7Je3PW8/7cDcxYtZXSiioAkiNh7jinP+cfmYOZBVylSNNQ11NSYxkKxwFFwDM1hYKZtQYmAqOccyvNrJ1zrua7tURRKMSnqirHhh0lLNtYzIPjFzNp6SbOHtyJ3507gFZJkaDLE2n0Ar9OwTn3iZnl7afJxcArzrmVfvsDBoLEr1DI6JieTMf0ZIZ2zeCRj5dw/wcLeW/Oeu/sJQdJiWEG57TmqLw29O3YiqLSCjYWlWLA+fmd9znF1TmnPQ2RvQR5TKEXEDGzCUAa8Ffn3DM1NTSz0cBogNzc3AYrUBqncMi47oQejOiewbgZa3EOzGDbrnK+WrmV/8zb925vk5du5sGLDicU8kLgs8Ub+fHz07ntrH6cNbhTQ6+CSKMVZCgkAEcCJwHJwCQzm+ycW7h3Q+fcY8Bj4HUfNWiV0mgdntuGw3Pb7DO9cEcpiwuKaJ0SISM1kVe+XMMf35lPbkYKN4/qw8QlG7nq6S8oKa/iN6/PZnj3DDJ14FoECDYUVgObnHPFQLGZfQIMBvYJBZH6yEprQVba11/y3z+uGys37+ThCUvYVVbJv79YRW7bFO48ZwCXPTGFO9+YywMXHR5gxSKNR5DXKbwOHGNmCWaWAgwF5gVYjzRTZsadZ/fnuF5ZjJm4nOw2yTx79TCGdcvguhN6MG7GWj6cv2+Xk0g8ilkomNlYYBLQ28xWm9lVZnatmV0L4JybB7wLzASmAP9wzs2OVT0S3xLCIf528eH8YlRvnrtmaPWexA9GdqdX+1R+/epsdugKahENnS3y5cotnPfwRDqmJ3PzaX04a1BHnZUkzU5dT0nVMBcS947IbcOzVw8jPTnCj8Z+xTf/PpHXvlpD4Y7SoEsTaXDaUxDxVVY5Xv5yNfe/v5D120sA6NMhjUE56XTJaEmXjBR6tEule1aqbgIkTU7gF6+JNDXhkHFBfmfOOyKHOWu388miQiYu2chHCwop3LG6ul1iOETP9qmc3Lc9PxjZXfd9kGZFewoidVBcWsGKTTtZVLCDuWu3M3P1NiYt3UTntsncefYA8vPa8N6cDbw+fQ0hM/7wrYF0ap0cdNki1QIf+yhWFArSWExasolbX5vFksJiEsMhyiqr6Nw2mS3F5SRFQjx86ZEcldc26DJFAIWCSIMoq6ji6YnLWbttF2cO6sQRua1ZUljENc9MY/WWnVw2PK96LwPgplN7cWQXBYU0PIWCSIC27SrnJ89/xUcLCslMTSS3bQprt5awYUcJFw/J5Rej+pCerNFdpeHoQLNIgNKTIzx15RBKyiurD0QXl1Zw/wcLeeqzZXwwdwOPX5bP4M6tA65UZE86r04khqLPTGrZIoHfnNmP1687hhaREBc+Non356wPsDqRfSkURBrYwJx0XvnB0fTu0Irv/2saYz5bFnRJItUUCiIByEprwfPXDOPkvu25/Y25vDVzXdAliQAKBZHAJCeGefiSIxick86tr83aY1iNhRt2cMk/JjN7zbZ93rdiUzGlFZUNWarEEYWCSIASwiHuu2AwxWWV/OrVWTjnWFJYxMWPf85nizdx5xtziT5DcOGGHZx038fc8+6CAKuW5kyhIBKwHu3S+Pkpvflg7gYe/HAxFz8+GXBcc2xXpizfzPh53u3LnXPc+tpsKqocY6esZNsuDfUth55CQaQR+N4xXcnv0ob7P1hIWUUVz149jF+M6kPXzJbc/e58Kqscr09fy5Rlm/nusC4Ul1UydsrKoMuWZkihINIIhEPG/Rccxqn92/Ovq4fSu0MakXCIn5/am0UFRYyZuJy73p7H4Jx0bj+7P8O7ZTDms+WUVVQFXbo0M3UKBTM7vy7TROTg5Wak8Oh38+nfKb162mkDOjC4c2t+++ZcNhaV8ttvDiAcMkYf143120t4a9baACuW5qiuewq/rOM0ETmEzIxbRvUB4KIhuQzK8a6APr5XFj3apfL4J8toakPVSOO232EuzOw04HQg28weiJrVCqiIZWEi4hnePYM3bziGXu3TqqeFQsbVx3TllldmMWnJJkb0yAywQmlODrSnsBaYCpQA06Ie44BTY1uaiOw2IDudxIQ9/3f95uHZtEtrwe/emqdjC3LI7DcUnHMznHNPAz2cc0/7z8cBi51zWxqkQhGpUVIkzF3nDmTuuu089OGioMuRZqKuxxQ+MLNWZtYW+BJ43Mz+HMO6RKQOvtGvPd86Ipu/TVjCzNVbgy5HmoG6hkK6c2478C3gGefcUOCk2JUlInV121n9yUptwY0vzKCkXMNfyP+mrqGQYGYdgQuAN2NYj4jUU3pyhLvPG8SigiIe+XhJ0OVIE1fXULgTeA9Y4pz7wsy6AerEFGkkju+VxYjuGbw7W/dnkP9NnULBOfeic26Qc+4H/uulzrlvx7Y0EamPEd0zmL9+B1uKy4IuRZqwul7RnGNmr5pZgf942cxyYl2ciNTdsG4ZAHy+bFPAlUhTVtfuo6fwTkXt5D/e8KeJSCMxKKc1yZEwk5YoFOTg1TUUspxzTznnKvzHGCArhnWJSD0lJoTIz2vD5KWbgy5FmrC6hsImM7vUzML+41JAP0dEGplh3TJYsGEHm4pKD9xYpAZ1DYXv4Z2Ouh5YB5wHXBGjmkTkIA3v7h1X0N6CHKz6nJJ6uXMuyznXDi8k7ohdWSJyMAZmp5OSGGbyUu3Iy8GpaygMih7ryDm3GTg8NiWJyMGKhEMcldeWSQoFOUh1DYWQmbXZ/cIfA2m/w26LSDCGdctgcUERhTt0XEHqr66hcB8wycx+a2a/BSYCf9rfG8zsSf+ahtkHaHeUmVWY2Xl1rEVE9uPr4wraW5D6q+sVzc/gDYa3wX98yzn3zwO8bQwwan8NzCwM3A28X5c6ROTABnRqRWqLBCYu2Rh0KdIE1XVPAefcXOfcQ/5jbh3afwIc6BSIG4CXgYK61iEi+5cQDnFin3aMm76WrTs15IXUT51D4VAzs2zgXODhOrQdbWZTzWxqYWFh7IsTaeJ+eEJ3issqeeqz5UGXIk1MYKEA/AW42Tl3wPsIOucec87lO+fys7J0IbXIgfTp0IpT+rXnqc+WsaOkPOhypAkJMhTygefNbDnexXB/N7NvBliPSLNyw4k92V5SwTOTVgRdijQhgYWCc66rcy7POZcHvAT80Dn3WlD1iDQ3A3PSGdk7iyf+u4ydZRVBlyNNRMxCwczGApOA3ma22syuMrNrzezaWH2miOzphhN7srm4jDETlwddijQRMbsAzTl3UT3aXhGrOkTi2ZFd2nB8ryz+9O4Cpq/cyi9P70vXzJZBlyWNWJDHFESkATxy6ZHcdEovPlu8kW/c/zH3vDefqioXdFnSSCkURJq55MQw15/Yk49+PpJzDsvmbx8t4VevzqJSwSA10PhFInGiXVoS954/iOzWSTzw4WLKKqr403mDSAjrt6F8TaEgEkfMjJ+d0psWkTD3vLcADO6/4LCgy5JGRD8RROLQdSf04OpjuvLKl2so2F4SdDnSiCgUROLUefk5AHy0QEOPydcUCiJxqnf7NLJbJzN+nkJBvqZQEIlTZsaJfdrx38UbKSmvDLocaSQUCiJx7MS+7dhZVsnnyw40yr3EC4WCSBwb3i2D5EiYD+dtCLoUaSQUCiJxLCkS5ugemYyfX4BzuphNFAoice+kvu1YvWUXiwqKgi5FGgGFgkicO6F3OwCdhSSAQkEk7nVIT2JAdis+nK/jCqJQEBHgG307MHXFFuas3RZ0KRIwhYKIcMXRebRJSeSOcXN1wDnOKRREhPTkCDed0pspyzfz5sx1QZcjAVIoiAgAFx7Vmf6dWvGHt+fpns5xTKEgIgCEQ8btZ/dn7bYSHpmwJOhyJCAKBRGpdlReW84e3IlHPl7KuBlrgy5HAqBQEJE93H52fwblpPOjsV/xh3fm6badcUahICJ7aNsykeeuGcbFQ3N59OOlXPX0F5RVVAVdljQQhYKI7CMxIcTvzx3IbWf1Y8KCQl77ak3QJUkDUSiISK2uGJFHv46teOSTJVSpGykuKBREpFZmxveP78bSwmL+o+G144JCQUT264yBHclpk8yjnywNuhRpAAoFEdmvhHCIa47txrQVW/hiue7Q1twpFETkgC7I70yblAiPfqyL2po7hYKIHFByYpjLR+Txn3kFGkm1mVMoiEidXDmiKxktE7n1tdk6E6kZUyiISJ2kp0T49Rl9+WrlVsZ+sTLociRGFAoiUmfnHp7N8G4Z3P3OfAp3lAZdjsSAQkFE6szM+N25Aygpr+Kut+YGXY7EgEJBROqle1Yq147szmvT1zJ2irqRmpuYhYKZPWlmBWY2u5b5l5jZTDObZWYTzWxwrGoRkUPrhyO7c1yvLH75yizuf3+BbuHZjCTEcNljgIeAZ2qZvww43jm3xcxOAx4DhsawHhE5RJIiYZ64PJ9bX53NAx8uZtWWXZw9uBOVVY5w2BjRPYMWCeGgy5SDELNQcM59YmZ5+5k/MerlZCAnVrWIyKEXCYf447cHktMmmfs+WMirUSOpnjagA3+/5AjMLMAK5WDEck+hPq4C3qltppmNBkYD5ObmNlRNInIAZsYNJ/XkzMGd2LarnLAZH8zbwAPjF/Hs5yu5dFiXoEuUego8FMzsBLxQOKa2Ns65x/C6l8jPz1fnpUgj0zWzZfXz/p1aMWPVVu58cy5HdmlD346tAqxM6ivQs4/MbBDwD+Ac59ymIGsRkUMjFDLuu2Aw6ckRrn/uS3aWVQRdktRDYKFgZrnAK8B3nXMLg6pDRA69zNQW/OXCw1i6sZhj7/6IX786i4lLNmp4jCbAYnUqmZmNBUYCmcAG4DYgAuCce8TM/gF8G1jhv6XCOZd/oOXm5+e7qVOnxqRmETm0Pl1UyL+/WMX4eQXsKq/kkqG53HXuwKDLiktmNq0u37ExC4VYUSiIND27yiq58825PP/FSt758bH06aDjDA2trqGgK5pFJOaSE8PcPKo3aS0S+OM784MuR/ZDoSAiDaJ1SiLXn9iDCQsK+WzxxqDLkVooFESkwVw2PI/s1sn84Z15OujcSCkURKTBJEXC3HRqL2av2c4/J6/QmEmNkEJBRBrUOYOzObJLG24bN4czH/wvb89aR6X2GhoNhYKINKhQyBh7zTD+dN4gdpVV8sNnv+SUP3/MuBlrFQ6NgE5JFZHAVFY53pm9jgfGL2LhhiJ6tkvl5lF9OLlf+6BLa3Z0SqqINHrhkHHmoE68++PjePCiw3HA1c9M5Y435lBWUVXdbldZJQU7SoIrNI4EPiCeiEgoZJw1uBOn9u/AH96Zx1OfLWf6qq2MPrYb78/dwHtz1lNZ5Rhz5RCGd88IutxmTd1HItLovDVzHTe/PJOi0gpaJSVw+sCOTFuxhTVbd/HPq4ZwZJe2QZfY5NS1+0h7CiLS6JwxqCODO6eztLCYod3a0iIhTMGOEi58dDJXPPkFz14zlEE5rfd4z7Zd5bw9ax3fPiKHxAT1jB8s/ZcTkUYpp00Kx/XKqr6tZ7u0JJ69eijpKREue3IKKzYVV7d1znHzSzP55SuzGDNxWVAl72P8vA2ccO8EikubzvDhCgURaTI6tU7m2au9W7mPfmZa9Zfti1NX8+6c9WS0TOShDxezpbgsyDKrPff5SpZtLGbuuu1Bl1JnCgURaVK6ZLTkoYuOYFHBDm56cQbLNhZz+xtzGNE9g39dPZSi0goe/HBxdXvnHNNXbWXbrvIGrXNHSTmfLvLGeJq7tumEgo4piEiTc0zPTH55Wl/uensek5ZuIhIOcd8Fg+mYnswF+Z355+TlXD6iC21aJnLTCzN4f+4GkiIhzhzUiYuG5HJklzYxr/HD+QWUVVZhBvOa0J6CQkFEmqSrj+3K7LXbeH36Wv4mYb0kAAANxklEQVR28RF0TE8G4Gff6MXr09dyy8uzWLttF2u27OKnJ/di/fYSxk1fw0vTVnPLaX249vjuMa3v3dnraZfWgu5ZqU2q+0ihICJNkplx7/mD+f5x3enX6eub9rRrlcTo47rx1/GLaN+qBc+PHkZ+nncK66/P6MuNL0zn3vcWcEyPTAZkp8ektl1llUxYUMh5R+bQIiHEPyevoKKyioRw4++xb/wViojUIhIO7REIu/1gZHduP6sfb/3o2OpAAEhtkcDd3x5ERmoiP/n3dErKK2NS18cLvduPnjagA/06taK0ooplG4sP/MZGQKEgIs1OUiTMFUd3JTO1xT7zWqckcu/5g1lcUBSzu8C9PWs9bVIiDOnalr4dvdBqKl1ICgURiTvH9sziihF5jJm4nI/mF+wz/40Za5m5eutBLbu0opIP5xdwav8OJIRDdM9KJRI2hYKISGN2y2l96NuxFTeM/Yo5a7dVTx/z2TJuGPsV1z33JeWVVftZQs3+u2gjRaUVjBrQAYDEhBA926Uxb92OQ1Z7LCkURCQuJUXCPHXFUaQlJXDlU1+wZusuXp++htvfmEufDmms2ryLV79cU+/lPvv5Stq2TGRE98zqaX07tmoy1yooFEQkbnVIT2LMlUPYVV7Jdx6bxI0vzGBI17a8dt3RDMpJ58GPFtVrb2HO2m18OL+A7x2dt8f4S/06tWJjUSmFO0pjsRqHlEJBROJa7w5pPPbdfDZsK6VX+zT+cXk+SZEwPzm5Z733Fh6esITUFgl8d3jeHtP7dkwDmsZFbAoFEYl7w7tn8MHPjuPFa4fTKikCwAm929Vrb2FpYRFvzVrHd4d3IT05sse8fk3oDCRdvCYigjemUjQz4ycn9+R7Y6Zy5xtz6Z7lzc9uk8JxvTKrR2/d7ZGPl5AYDvG9o7vus+zWKYl0Sk9qEnsKCgURkVqc0LsdR3Zpwz8nr9hjelpSAqP6d+D43lm0S0siHIJXvlzDJUNzyUrb99oI8I4rNIWDzQoFEZFamBnPjx7G9l3lmBkAs9ZsY9z0tbwzez0vTltd3TYhZFxzXLdal9W3Yys+WlBISXklSZFwre2CplAQEdmPSDhERtSV0cf3yuL4XlncVT6AZRuL2VhUysaiUtqnJZHTJqXW5eTntaWyajH3vLeA35zZryFKPygKBRGRg5AUCVcPYVEXx/XM5IoReTzx32V0TE/i6mO9vYqqKseSwiJ6tEut3hsJkkJBRKQBmBm/ObMfBTtK+N1b80htkUBRaQX/nLyCFZt2cuc5/blsr1NZg6BQEBFpIOGQcf8Fh7FxxxRueWUWAPld2tAqKcJ97y/krEGdaNMyMdAaFQoiIg0oKRLm8cvy+dfnKxjZO4v+ndJZsH4Hpz/wKfd9sIDffXNgoPXF7OI1M3vSzArMbHYt883MHjCzxWY208yOiFUtIiKNSXpKhOtO6EH/Tt5Nfnp3SOPSobk89/nKwE9bjeUVzWOAUfuZfxrQ03+MBh6OYS0iIo3aT7/Ri/TkCHe8MYcJCwq4+aWZHHP3h9z2+mx2lcXmZkA1iVkoOOc+ATbvp8k5wDPOMxlobWYdY1WPiEhj1jolkZ+d0pvPl23miqe+4K1Z6+iSkcLTk1ZwxgOfMmPVwd3fob6CPKaQDayKer3an7YumHJERIJ18ZBcSssr6ZrZkqN7ZJIUCTNx8UZuenEG33p4Ir86vS9XHbPvMBqHUpM40Gxmo/G6mMjNzQ24GhGR2AiHrPr6hd1G9MjknZ8cxx3j5tA1s/aL4w6VIENhDdA56nWOP20fzrnHgMcA8vPzXexLExFpPNKTI9x/4WEN8llBDp09DrjMPwtpGLDNOaeuIxGRAMVsT8HMxgIjgUwzWw3cBkQAnHOPAG8DpwOLgZ3AlbGqRURE6iZmoeCcu+gA8x1wXaw+X0RE6k93XhMRkWoKBRERqaZQEBGRagoFERGpplAQEZFq5p0E1HSYWSGw4oANa5YJbDyE5TQV8bje8bjOEJ/rHY/rDPVf7y7OuawDNWpyofC/MLOpzrn8oOtoaPG43vG4zhCf6x2P6wyxW291H4mISDWFgoiIVIu3UHgs6AICEo/rHY/rDPG53vG4zhCj9Y6rYwoiIrJ/8banICIi+6FQEBGRanETCmY2yswWmNliM7sl6Hpiwcw6m9lHZjbXzOaY2Y/96W3N7AMzW+T/bRN0rbFgZmEz+8rM3vRfdzWzz/1t/m8zSwy6xkPJzFqb2UtmNt/M5pnZ8HjY1mb2U//f92wzG2tmSc1xW5vZk2ZWYGazo6bVuH39+9I84K//TDM74mA/Ny5CwczCwN+A04B+wEVm1i/YqmKiArjROdcPGAZc56/nLcB451xPYLz/ujn6MTAv6vXdwJ+dcz2ALcBVgVQVO38F3nXO9QEG4617s97WZpYN/AjId84NAMLAd2ie23oMMGqvabVt39OAnv5jNPDwwX5oXIQCMARY7Jxb6pwrA54Hzgm4pkPOObfOOfel/3wH3pdENt66Pu03exr4ZjAVxo6Z5QBnAP/wXxtwIvCS36RZrbeZpQPHAU8AOOfKnHNbiYNtjXcfmGQzSwBSgHU0w23tnPsE2LzX5Nq27znAM84zGWhtZh0P5nPjJRSygVVRr1f705otM8sDDgc+B9pH3ep0PdA+oLJi6S/AL4Aq/3UGsNU5V+G/bm7bvCtQCDzld5n9w8xa0sy3tXNuDXAvsBIvDLYB02je2zpabdv3kH3HxUsoxBUzSwVeBn7inNsePc+/412zOg/ZzM4ECpxz04KupQElAEcADzvnDgeK2aurqJlu6zZ4v4q7Ap2AluzbxRIXYrV94yUU1gCdo17n+NOaHTOL4AXCs865V/zJG3bvSvp/C4KqL0aOBs42s+V4XYMn4vW3t/a7GKD5bfPVwGrn3Of+65fwQqK5b+uTgWXOuULnXDnwCt72b87bOlpt2/eQfcfFSyh8AfT0z1BIxDswNS7gmg45vx/9CWCec+7+qFnjgMv955cDrzd0bbHknPulcy7HOZeHt20/dM5dAnwEnOc3a1br7ZxbD6wys97+pJOAuTTzbY3XbTTMzFL8f++717vZbuu91LZ9xwGX+WchDQO2RXUz1UvcXNFsZqfj9TuHgSedc3cFXNIhZ2bHAJ8Cs/i6b/1XeMcVXgBy8YYdv8A5t/cBrGbBzEYCNznnzjSzbnh7Dm2Br4BLnXOlQdZ3KJnZYXgH1hOBpcCVeD/0mvW2NrM7gAvxzrb7Crgar/+8WW1rMxsLjMQbInsDcBvwGjVsXz8gH8LrStsJXOmcm3pQnxsvoSAiIgcWL91HIiJSBwoFERGpplAQEZFqCgUREammUBARkWoKBWk0zGyi/zfPzC4+xMv+VU2fFStm9k0z+78YLftXB25V72UONLMxh3q50vTolFRpdKKvNajHexKixr6paX6Rcy71UNRXx3omAmc75zb+j8vZZ71itS5m9h/ge865lYd62dJ0aE9BGg0zK/Kf/hE41sym+2Pnh83sHjP7wh8r/vt++5Fm9qmZjcO7qhUze83Mpvnj7Y/2p/0Rb1TN6Wb2bPRn+VeA3uOPzT/LzC6MWvYE+/p+Bc/6FwhhZn80754VM83s3hrWoxdQujsQzGyMmT1iZlPNbKE/VtPu+z/Uab2ill3TulxqZlP8aY/6Q8VjZkVmdpeZzTCzyWbW3p9+vr++M8zsk6jFv4F3RbjEM+ecHno0igdQ5P8dCbwZNX00cKv/vAUwFW9AtJF4A8F1jWrb1v+bDMwGMqKXXcNnfRv4AO9K9/Z4wyh09Je9DW8MmRAwCTgGb/TVBXy9l926hvW4Ergv6vUY4F1/OT3xxi1Kqs961VS7/7wv3pd5xH/9d+Ay/7kDzvKf/ynqs2YB2XvXjzeG0BtB/zvQI9jH7gGkRBqzU4BBZrZ7bJt0vC/XMmCKc25ZVNsfmdm5/vPOfrtN+1n2McBY51wl3mBjHwNHAdv9Za8GMLPpQB4wGSgBnjDvDm9v1rDMjnjDWkd7wTlXBSwys6VAn3quV21OAo4EvvB3ZJL5epC0sqj6pgHf8J9/BowxsxfwBpTbrQBv5FGJYwoFaQoMuME5994eE71jD8V7vT4ZGO6c22lmE/B+kR+s6LFzKoEE51yFmQ3B+zI+D7geb1TWaLvwvuCj7X3wzlHH9ToAA552zv2yhnnlzrndn1uJ//+7c+5aMxuKd1OiaWZ2pHNuE95/q111/FxppnRMQRqjHUBa1Ov3gB+YNyw4ZtbLvBvK7C0d2OIHQh+8W5LuVr77/Xv5FLjQ79/Pwrub2ZTaCjPvXhXpzrm3gZ/i3QZzb/OAHntNO9/MQmbWHeiG1wVV1/XaW/S6jAfOM7N2/jLamlmX/b3ZzLo75z53zv0f3h7N7iGXe+F1uUkc056CNEYzgUozm4HXH/9XvK6bL/2DvYXUfLvFd4FrzWwe3pfu5Kh5jwEzzexL5w2rvdurwHBgBt6v918459b7oVKTNOB1M0vC+5X+sxrafALcZ2YW9Ut9JV7YtAKudc6VmNk/6rhee9tjXczsVuB9MwsB5cB1eCNo1uYeM+vp1z/eX3eAE4C36vD50ozplFSRGDCzv+IdtP2Pf/7/m865lw7wtsCYWQvgY+AYt59Te6X5U/eRSGz8Hu+m8k1FLnCLAkG0pyAiItW0pyAiItUUCiIiUk2hICIi1RQKIiJSTaEgIiLV/h9TJNoFq5UxWgAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -788,8 +777,8 @@ "output_type": "stream", "text": [ "Tensor(\"Mean_1:0\", shape=(), dtype=float32)\n", - "Train Accuracy: 0.6638889\n", - "Test Accuracy: 0.55\n" + "Train Accuracy: 0.94074076\n", + "Test Accuracy: 0.78333336\n" ] } ], @@ -887,7 +876,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.5.2" } }, "nbformat": 4, From f4c7744f1cd12e29b30805872557bef7bfe0d11f Mon Sep 17 00:00:00 2001 From: ArtemShaprio Date: Mon, 27 May 2019 15:48:23 +0300 Subject: [PATCH 3/3] Task CNN lesson --- cnn/CNN_lesson.ipynb | 1098 ++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 1066 insertions(+), 32 deletions(-) diff --git a/cnn/CNN_lesson.ipynb b/cnn/CNN_lesson.ipynb index cf6aa1c..02c06e1 100644 --- a/cnn/CNN_lesson.ipynb +++ b/cnn/CNN_lesson.ipynb @@ -2,22 +2,22 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -42,22 +42,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -95,22 +95,22 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -133,24 +133,46 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n", + "32768/29515 [=================================] - 0s 1us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n", + "26427392/26421880 [==============================] - 2s 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n", + "8192/5148 [===============================================] - 0s 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n", + "4423680/4422102 [==============================] - 0s 0us/step\n", + "WARNING:tensorflow:From /home/user/miniconda3/envs/ml_course/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n", "Epoch 1/5\n", - "60000/60000 [==============================] - 7s 114us/sample - loss: 0.7427 - acc: 0.7618\n", + "60000/60000 [==============================] - 3s 53us/sample - loss: 0.7606 - acc: 0.7504\n", "Epoch 2/5\n", - "60000/60000 [==============================] - 6s 92us/sample - loss: 0.5129 - acc: 0.8251\n", + "60000/60000 [==============================] - 3s 51us/sample - loss: 0.5213 - acc: 0.8237\n", "Epoch 3/5\n", - "60000/60000 [==============================] - 6s 93us/sample - loss: 0.4671 - acc: 0.8403\n", + "60000/60000 [==============================] - 3s 51us/sample - loss: 0.4748 - acc: 0.8375\n", "Epoch 4/5\n", - "60000/60000 [==============================] - 6s 94us/sample - loss: 0.4421 - acc: 0.8484\n", + "60000/60000 [==============================] - 3s 50us/sample - loss: 0.4477 - acc: 0.8457\n", "Epoch 5/5\n", - "60000/60000 [==============================] - 5s 89us/sample - loss: 0.4240 - acc: 0.8531\n" + "60000/60000 [==============================] - 3s 50us/sample - loss: 0.4300 - acc: 0.8523\n", + "10000/10000 [==============================] - 0s 26us/sample - loss: 0.4584 - acc: 0.8403\n" ] + }, + { + "data": { + "text/plain": [ + "[0.45836426692008975, 0.8403]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -174,22 +196,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 14, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -206,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -214,14 +236,27 @@ "output_type": "stream", "text": [ "Epoch 1/5\n", - "60000/60000 [==============================] - 61s 1ms/sample - loss: 0.8694 - acc: 0.6835\n", + "60000/60000 [==============================] - 20s 341us/sample - loss: 0.9134 - acc: 0.6703\n", "Epoch 2/5\n", - "60000/60000 [==============================] - 52s 874us/sample - loss: 0.5448 - acc: 0.7978\n", + "60000/60000 [==============================] - 20s 335us/sample - loss: 0.5585 - acc: 0.7906\n", "Epoch 3/5\n", - "60000/60000 [==============================] - 58s 973us/sample - loss: 0.4754 - acc: 0.8267\n", + "60000/60000 [==============================] - 20s 338us/sample - loss: 0.4861 - acc: 0.8213\n", "Epoch 4/5\n", - "29024/60000 [=============>................] - ETA: 32s - loss: 0.4403 - acc: 0.8412" + "60000/60000 [==============================] - 21s 347us/sample - loss: 0.4430 - acc: 0.8381\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 20s 341us/sample - loss: 0.4140 - acc: 0.8491\n", + "10000/10000 [==============================] - 1s 121us/sample - loss: 0.4194 - acc: 0.8494\n" ] + }, + { + "data": { + "text/plain": [ + "[0.4193917732715607, 0.8494]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -245,22 +280,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 18, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -288,6 +323,1005 @@ " - Remove all Convolutions but the first. What impact do you think this will have? Experiment with it.\n", " - In the previous lesson you implemented a callback to check on the loss function and to cancel training once it hit a certain amount. See if you can implement that here!" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Change number of filters\n", + "===" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 14s 234us/sample - loss: 0.8786 - acc: 0.6822\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 14s 234us/sample - loss: 0.5557 - acc: 0.7921\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 14s 237us/sample - loss: 0.4825 - acc: 0.8248\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 14s 237us/sample - loss: 0.4397 - acc: 0.8420\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 14s 237us/sample - loss: 0.4119 - acc: 0.8508\n", + "10000/10000 [==============================] - 1s 91us/sample - loss: 0.4253 - acc: 0.8462\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.4252513979673386, 0.8462]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of filters = 16 both layers\n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Conv2D(16, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 17s 279us/sample - loss: 0.8339 - acc: 0.6957\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 17s 277us/sample - loss: 0.5321 - acc: 0.8029\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 17s 276us/sample - loss: 0.4609 - acc: 0.8325\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 18s 296us/sample - loss: 0.4203 - acc: 0.8467\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 17s 277us/sample - loss: 0.3930 - acc: 0.8591\n", + "10000/10000 [==============================] - 1s 106us/sample - loss: 0.4139 - acc: 0.8514\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.4138932249546051, 0.8514]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of filters = 16 1st layer\n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 44s 733us/sample - loss: 0.9115 - acc: 0.6680\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 44s 735us/sample - loss: 0.5396 - acc: 0.8004\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 43s 724us/sample - loss: 0.4588 - acc: 0.8348\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 44s 726us/sample - loss: 0.4144 - acc: 0.8511\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 44s 735us/sample - loss: 0.3848 - acc: 0.8616\n", + "10000/10000 [==============================] - 2s 218us/sample - loss: 0.4014 - acc: 0.8592\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.40140494065284726, 0.8592]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of filters = 64 both layers\n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 26s 441us/sample - loss: 0.8702 - acc: 0.6840\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 25s 420us/sample - loss: 0.5385 - acc: 0.8009\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 26s 429us/sample - loss: 0.4627 - acc: 0.8315\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 25s 418us/sample - loss: 0.4203 - acc: 0.8471\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 25s 413us/sample - loss: 0.3899 - acc: 0.8584\n", + "10000/10000 [==============================] - 1s 149us/sample - loss: 0.4070 - acc: 0.8559\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.40703206396102903, 0.8559]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of filters = 64 2nd layer\n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Change number of conv layers\n", + "===" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 17s 288us/sample - loss: 0.6930 - acc: 0.7561\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 18s 306us/sample - loss: 0.4844 - acc: 0.8273\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 17s 289us/sample - loss: 0.4324 - acc: 0.8451\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 18s 297us/sample - loss: 0.3975 - acc: 0.8589\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 18s 297us/sample - loss: 0.3730 - acc: 0.8668\n", + "10000/10000 [==============================] - 1s 114us/sample - loss: 0.3849 - acc: 0.8645\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.38492013049125673, 0.8645]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remove last conv layer\n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 21s 357us/sample - loss: 1.3447 - acc: 0.5296\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 21s 355us/sample - loss: 0.6942 - acc: 0.7433\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 21s 356us/sample - loss: 0.5972 - acc: 0.7812\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 21s 352us/sample - loss: 0.5471 - acc: 0.8016\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 21s 356us/sample - loss: 0.5144 - acc: 0.8140\n", + "10000/10000 [==============================] - 1s 132us/sample - loss: 0.5099 - acc: 0.8205\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.5098958879947663, 0.8205]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Adding 1 conv layer, 1 Pooling layer\n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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TYd7eddjrOCKSoE5b7s65QeAB4C9APeGjYj4ys++a2U2RYf8R+IaZvQf8Eviq06LwlPnikgqKstNYs16zdxGZWEo0g5xzLxLeUTr6tm+PurwVuDq20WQyGWlB7r6uiv/+4jY27znCZXNneB1JRBKMPqGapL50xVwKsjR7F5GJqdyTVFYohTuvmcfL29t4b+9Rr+OISIJRuSexryydS15GKmvWN3gdRUQSjMo9ieWkp3LnNfNYW9/Ch/s6vI4jIglE5Z7k7riqkpz0FB7R7F1ERlG5J7m8jFS+dlUlf/7oINsOdnodR0QShMrdB75+zTyy0oKavYvICJW7D+RnpnHHVZX88YMDNLR2eR1HRBKAyt0n7rq2ioxUzd5FJEzl7hMFWWncfuVcnn9vP01t3V7HERGPqdx95K5rq0hLCfDohkavo4iIx1TuPlKcE+KLS+byuy37+Li9x+s4IuIhlbvP3HN9FcGA8djLWnsXmc5U7j5TmpvObZfP4dnNzTQf0exdZLpSufvQvcuqCZjx+MtaexeZrlTuPjQrL4NbFpfzm7pmDnQc9zqOiHhA5e5T37y+mmHn+N7GJq+jiIgHVO4+Nacgk89fWs4v3v6Y1s5er+OISJyp3H3svuXVDA07vveKZu8i043K3cfmFmZx88Wz+flbe3imbi+Huvu8jiQicRLVCbIlef3TyvOo232Ef372fczg0ooZrFpYyqqFJdSUZGNmXkcUkSlgzjlPHnjx4sWurq7Ok8eebpxzfLS/k3X1raytb+GDyFmb5hZmsmphKSsXlnB5ZQGpQb2RE0l0ZrbZObf4tONU7tPPwY5e1m1rYe3WFl5vbKd/cJjc9BSWLyhh1cJSrp9fTG56qtcxRWQCKneJyrG+QV7deYh19S2s39ZK+7F+UgLGFVUFkeWbUuYUZHodU0QiVO5yxoaGHVv2HuGlra2sq29hZ2v4q4Pnl+awalF4Vn9ReT6BgNbpRbyicpdztvvQMdbWt7CuvpW3dx9maNhRlB1i5YISVi0q5ZqaIjLSgl7HFJlWVO4SUx09A7y8o5WXtrawcXsbXX2DhFICXFNTxKpFpaxcUEJJbrrXMUV8L9py16GQEpW8zFRuvriMmy8uo39wmHd2H+alrS3hmf22VgAuKs8Lr9MvKmXBzBwdZiniIc3c5Zw459jR0s3a+hZe2trClr1HASjLz2DVwvDyzRXzCklL0WGWIrGgZRnxRGtXLxu2tfLS1lZea2ijd2CY7FAK188vZtXCEpbPLyE/M83rmCJJS+UunjveP8QbjYdYW9/C2vpW2rr6CAaMxXNnjCzfzCvK8jqmSFJRuUtCGR52fLCvY2T5ZtvBLgCqi7NYtSh8PP2lFTMI6jBLkVNSuUtCaz7SM/J1CJua2hkYchRkpbF8fgmrFpZw7XnFZIe0v19kPJW7JI3O3gFe2dHGuvpW1m9rpeP4AGnBAEurCyOz+hJm5WV4HVMkIajcJSkNDg1Tt+cIayOHWe5uD5/k+/zZuaxaWMqnF5Vy/uxcHWYp05bKXZKec47GthOfkm1h854jDDuYmZvOyshhlkurCklP1adkZfpQuYvvtHf3sWF7G+vqW9i4o42e/iEy04JcW1vEqoWlrFhQQmF2yOuYIlNK5S6+1jswxKam9pHvvjnQ0TvmZCSfXlRCdbFORiL+o3KXaePEyUjCx9O38OG+TuDkyUhWLSzl8soZpOhkJOIDMS13M7sBeAgIAk855/7nBGO+AHwHcMB7zrkvnup3qtxlqhzoOD5ymOUbDe30Dw2Tl5HKsvnFOhmJJL2YlbuZBYEdwKeBZuAd4Dbn3NZRY2qBZ4AVzrkjZlbinGs91e9VuUs8nDgZydrIyUgOR05GcmVVYXinrE5GIkkmluW+FPiOc+5vI9f/BcA59z9GjflfwA7n3FPRBlS5S7yNPhnJ2voWGiInI1kwM2ek6HUyEkl0sfzK3zJg76jrzcAV48acF3nQ1wkv3XzHOffnKLOKxEUwYFw2t4DL5hbwrc8sGDkZydr6Fp7Y2MSjGxopyg6xYkExF5TnU12cRU1xNsU5Ie2YlaQTTblP9L96/HQ/BagFlgHlwKtm9inn3NExv8jsbuBugIqKijMOKxJLlUVZ3HVtFXddW8XRnn427mjjpa0t/OnDgzxT1zwyLieUQlVJNtXFWVQXZ1NdnE1NSTZzCzNJ1U5aSVDRlHszMGfU9XJg/wRjNjnnBoBdZradcNm/M3qQc+5J4EkIL8ucbWiRWMvPTBs5GYlzjpbOPhrbumlo7aaxLfzzRkM7z/1138ifSQkYFYWZI4VfXZxFdUn4cl6GdtiKt6Ip93eAWjObB+wDbgXGHwnzO+A24EdmVkR4maYplkFF4sXMmJmXzsy8dK6uKRpzX3ffIE2Rsm9sPTZS/C9vb2Vg6OR8pSg7NKbsT8z6y/IztKYvcXHacnfODZrZA8BfCK+nP+2c+8jMvgvUOeeej9z3N2a2FRgC/pNzrn0qg4t4ITuUwoXl+VxYnj/m9sGhYZqPHB8z029sO8Yf3z9Ax/GBkXHpqQGqirIjpX9ymaeqOEtfoyAxpQ8xiUwh5xyHj/XT2BaZ5beeLP69R3o48fQzC5+acGSJpyRrZG2/MCtNO3RlhE6QLZIAzIzC7BCF2SGWzCsYc1/vwBC7Dh37xBLP27sOc3xgaGRcXkbqyVn+qGWeioJMfepWJqVyF/FIemqQhbNyWTgrd8ztw8OOA529o2b54fLfuKON32w+eRRPatCYW5j1iaN4qoqzyNEncKc9lbtIggkEjLL8DMryM7juvOIx93UcH4js0D25zNPQ2s26+lYGh08usZbmhiY8imdWXrqWeKYJlbtIEsnLSOWSihlcUjFjzO0DQ8N8fLgnMts/NrJj93db9tHVOzgyLjMtSNW4mX51cfiYfe3Q9ReVu4gPpAYDI4U9mnOOtu6+MWv6jW3HqNt9hN9vOflxlYDBnILMMYdtnpjtF2SlxfuvIzGgchfxMTOjJCedkpx0llYXjrmvp38wskP35Ey/sbWb1xsO0Tc4PDJuRmbq2Jl+5Eie8hmZBHXMfsJSuYtMU5lpKZw/O4/zZ+eNuX1o2LH/6HEaRg7dDM/6121r4dd1J79mKi0YYF5RFlXFWZTlZ1Cam05JboiZuemURn4y0rTU4xWVu4iMEQwYcwoymVOQyfL5JWPuO9oTOWZ/1JE82w92sWF7K70Dw5/4XTnpKSNlX5IbCpd+ToiZeemURG4vzg6RlqJDOmNN5S4iUcvPTOOyuWlcNnfsDl3nHJ29g7R29tLS2UdLZy8tXb20dESud/XyVtMxWjp7xxzVc0JRdholOemURl4ASnLTIy8KoZEXhqKskL664Qyo3EXknJkZeRmp5GWkUluaM+m44WHH4Z5+Wjp7aY28CByMvCC0Rl4QPtjXSfuxPsZ/eD4YMEpyQuEZf0649GfmpVMSuVwaeTHIy0jV4Z6o3EUkjgIBoyg7RFF2iPNnTz5uYGiYQ919J98FjPyEr+9p7+GtXYfHfG/PCaGUwEjRf+IdQE74BaE0N0Rmmr/rz99/OxFJSqnBALPyMpiVl3HKcb0DQ+F3AF29HOwIvwC0dp18Qdi6v5P19a1jvs7hhJxQysh+gJm5J/YBhEZeGEpz0ynOCRFKSc6dwip3EUla6alBKgozqSic/Dy4zjm6+wbHvQvoi7wQhF8U3tp1mNau3jFf23xCQVYaJZGdwKU5J98RlI56V1CYHUq4w0JV7iLia2ZGTnoqOemp1JRkTzpueNhx9PhA+B1AV+/YncORy1v3d3Kou4/x+4QDBsXj1v7DLwTplOadvJ6fGb/9ASp3ERHC+wMKstIoyEpjEbmTjhscGqb9WP/IMlBLV3hncPhFoY+9h3uo232YIz2f3B+QlhKgJCfEHUsr+cZ1VVP511G5i4iciZRgYGSGfiq9A0O0jaz/jz08tCQ3NPU5p/wRRESmofTU4MiHwbygj4WJiPiQyl1ExIdU7iIiPqRyFxHxIZW7iIgPqdxFRHxI5S4i4kMqdxERHzI3/kuT4/XAZm3AnrP840XAoRjGiZVEzQWJm025zoxynRk/5prrnCs+3SDPyv1cmFmdc26x1znGS9RckLjZlOvMKNeZmc65tCwjIuJDKncRER9K1nJ/0usAk0jUXJC42ZTrzCjXmZm2uZJyzV1ERE4tWWfuIiJyCgld7mZ2g5ltN7MGM/vWBPeHzOzXkfvfMrPKBMn1VTNrM7MtkZ+74pTraTNrNbMPJ7nfzOzhSO73zezSBMm1zMw6Rm2vb8ch0xwz22Bm9Wb2kZn9+wnGxH17RZkr7tsr8rjpZva2mb0XyfZvE4yJ+3MyylxePSeDZvaumb0wwX1Tu62ccwn5AwSBRqAKSAPeAxaNG3Mf8ETk8q3ArxMk11eBRzzYZtcBlwIfTnL/jcCfAAOuBN5KkFzLgBfivK1mAZdGLucAOyb4d4z79ooyV9y3V+RxDciOXE4F3gKuHDfGi+dkNLm8ek7+B+AXE/17TfW2SuSZ+xKgwTnX5JzrB34F3DxuzM3AjyOXnwVW2tSffTaaXJ5wzr0CHD7FkJuBn7iwTUC+mc1KgFxx55w74Jz7a+RyF1APlI0bFvftFWUuT0S2Q3fkamrkZ/xOu7g/J6PMFXdmVg78HfDUJEOmdFslcrmXAXtHXW/mk//JR8Y45waBDqAwAXIBfD7yVv5ZM5szxZmiFW12LyyNvK3+k5mdH88HjrwdvoTwjG80T7fXKXKBR9srssywBWgFXnLOTbrN4vicjCYXxP85+X+AfwaGJ7l/SrdVIpf7RK9g41+NoxkTa9E85h+ASufchcBaTr46e82L7RWNvxL+SPVFwBrgd/F6YDPLBv4v8E/Ouc7xd0/wR+KyvU6Ty7Pt5Zwbcs5dDJQDS8zsU+OGeLLNosgV1+ekmX0WaHXObT7VsAlui9m2SuRybwZGv7qWA/snG2NmKUAeU//2/7S5nHPtzrm+yNXvA5dNcaZoRbNN484513nibbVz7kUg1cyKpvpxzSyVcIH+3Dn33ARDPNlep8vl1fYal+Eo8DJww7i7vHhOnjaXB8/Jq4GbzGw34aXbFWb2s3FjpnRbJXK5vwPUmtk8M0sjvMPh+XFjngfuiFy+BVjvInsnvMw1bl32JsLrpongeeArkaNArgQ6nHMHvA5lZjNPrDWa2RLC/y/bp/gxDfgBUO+c+9+TDIv79oomlxfbK/JYxWaWH7mcAawCto0bFvfnZDS54v2cdM79i3Ou3DlXSbgj1jvnvjxu2JRuq5RY/aJYc84NmtkDwF8IH6HytHPuIzP7LlDnnHue8JPgp2bWQPgV79YEybXazG4CBiO5vjrVuQDM7JeEj6QoMrNm4L8S3rmEc+4J4EXCR4A0AD3A1xIk1y3AN81sEDgO3BqHF+mrgduBDyJrtQD/ClSMyuXF9oomlxfbC8JH8vzYzIKEX1Cecc694PVzMspcnjwnx4vnttInVEVEfCiRl2VEROQsqdxFRHxI5S4i4kMqdxERH1K5i4j4kMpdRMSHVO4iIj6kchcR8aH/D/jaBfmYL5NEAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 57s 943us/sample - loss: 0.8162 - acc: 0.6989\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 58s 958us/sample - loss: 0.4972 - acc: 0.8161\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 57s 943us/sample - loss: 0.4300 - acc: 0.8430\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 56s 929us/sample - loss: 0.3908 - acc: 0.8585\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 55s 915us/sample - loss: 0.3616 - acc: 0.8696\n", + "10000/10000 [==============================] - 3s 266us/sample - loss: 0.3861 - acc: 0.8616\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.38610689671039583, 0.8616]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Adding 1 conv layer before 1st pooling layer \n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 61s 1ms/sample - loss: 0.8309 - acc: 0.6995\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 59s 988us/sample - loss: 0.4960 - acc: 0.8177\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 61s 1ms/sample - loss: 0.4335 - acc: 0.8411\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 60s 997us/sample - loss: 0.3962 - acc: 0.8558\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 62s 1ms/sample - loss: 0.3685 - acc: 0.8656\n", + "10000/10000 [==============================] - 3s 288us/sample - loss: 0.4003 - acc: 0.8534\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.40025168418884277, 0.8534]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Adding 1 conv layer before 1st pooling layer and 1 conv layer before 2nd pooling layer\n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 53s 878us/sample - loss: 0.7019 - acc: 0.7475\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 53s 885us/sample - loss: 0.4579 - acc: 0.8349\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 51s 842us/sample - loss: 0.3846 - acc: 0.8625\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 54s 893us/sample - loss: 0.3428 - acc: 0.8773\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 53s 887us/sample - loss: 0.3141 - acc: 0.8866\n", + "10000/10000 [==============================] - 3s 251us/sample - loss: 0.3376 - acc: 0.8799\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.33755967271327975, 0.8799]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remove last conv layer with pooling and add 1 conv layer before 1st pooling layer\n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 84s 1ms/sample - loss: 0.7026 - acc: 0.7486\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 79s 1ms/sample - loss: 0.4551 - acc: 0.8367\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 81s 1ms/sample - loss: 0.3830 - acc: 0.8634\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 81s 1ms/sample - loss: 0.3410 - acc: 0.8781\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 80s 1ms/sample - loss: 0.3123 - acc: 0.8873\n", + "10000/10000 [==============================] - 4s 397us/sample - loss: 0.3234 - acc: 0.8844\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.32344726518392564, 0.8844]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Increase number of filters in 2nd conv layer\n", + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(x_train, y_train, epochs=5)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Callback implementation\n", + "===" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "class CustomEarlyStopping(tf.keras.callbacks.Callback):\n", + " def __init__(self, value=0.2):\n", + " super(tf.keras.callbacks.Callback, self).__init__()\n", + " self.value = value\n", + " \n", + " def on_epoch_end(self, epoch, logs={}):\n", + " current = logs.get('loss')\n", + "\n", + " if current < self.value:\n", + " print(f\"\\nEpoch {epoch}, loss {current}: early stopping!\")\n", + " self.model.stop_training = True" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "60000/60000 [==============================] - 81s 1ms/sample - loss: 0.6894 - acc: 0.7508\n", + "Epoch 2/100\n", + "60000/60000 [==============================] - 78s 1ms/sample - loss: 0.4659 - acc: 0.8314\n", + "Epoch 3/100\n", + "60000/60000 [==============================] - 78s 1ms/sample - loss: 0.3946 - acc: 0.8586\n", + "Epoch 4/100\n", + "60000/60000 [==============================] - 78s 1ms/sample - loss: 0.3522 - acc: 0.8738\n", + "Epoch 5/100\n", + "60000/60000 [==============================] - 81s 1ms/sample - loss: 0.3212 - acc: 0.8846\n", + "Epoch 6/100\n", + "60000/60000 [==============================] - 81s 1ms/sample - loss: 0.2972 - acc: 0.8917\n", + "Epoch 7/100\n", + "60000/60000 [==============================] - 79s 1ms/sample - loss: 0.2767 - acc: 0.8996\n", + "Epoch 8/100\n", + "60000/60000 [==============================] - 78s 1ms/sample - loss: 0.2602 - acc: 0.9047\n", + "Epoch 9/100\n", + "60000/60000 [==============================] - 89s 1ms/sample - loss: 0.2456 - acc: 0.9105\n", + "Epoch 10/100\n", + "60000/60000 [==============================] - 82s 1ms/sample - loss: 0.2344 - acc: 0.9142\n", + "Epoch 11/100\n", + "60000/60000 [==============================] - 80s 1ms/sample - loss: 0.2227 - acc: 0.9192\n", + "Epoch 12/100\n", + "60000/60000 [==============================] - 81s 1ms/sample - loss: 0.2135 - acc: 0.9218\n", + "Epoch 13/100\n", + "60000/60000 [==============================] - 80s 1ms/sample - loss: 0.2034 - acc: 0.9260\n", + "Epoch 14/100\n", + "59968/60000 [============================>.] - ETA: 0s - loss: 0.1940 - acc: 0.9292\n", + "Epoch 13, loss 0.19395711921205122: early stopping!\n", + "60000/60000 [==============================] - 80s 1ms/sample - loss: 0.1940 - acc: 0.9292\n", + "10000/10000 [==============================] - 4s 427us/sample - loss: 0.2764 - acc: 0.9031\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.2763550966382027, 0.9031]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train = x_train.reshape(60000, 28, 28, 1)\n", + "x_test = x_test.reshape(10000, 28, 28, 1)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPool2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + "])\n", + "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "callbacks = [CustomEarlyStopping()]\n", + "\n", + "history = model.fit(x_train, y_train, epochs=100, callbacks=callbacks)\n", + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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