From 8a5a0ecff4d829817021c6610df239f5f0aa6a40 Mon Sep 17 00:00:00 2001 From: LordSomen Date: Wed, 29 Aug 2018 12:08:19 +0530 Subject: [PATCH] implemented modularity and shared variables --- Tensorflow/tf.py | 201 +++++++++++++++++- .../events.out.tfevents.1535512938.LordSomen | Bin 0 -> 27347 bytes .../events.out.tfevents.1535517249.LordSomen | Bin 0 -> 18019 bytes .../events.out.tfevents.1535517285.LordSomen | Bin 0 -> 24281 bytes .../events.out.tfevents.1535524640.LordSomen | Bin 0 -> 25115 bytes .../events.out.tfevents.1535524643.LordSomen | Bin 0 -> 24939 bytes .../events.out.tfevents.1535524650.LordSomen | Bin 0 -> 24939 bytes .../events.out.tfevents.1535512895.LordSomen | Bin 0 -> 26987 bytes 8 files changed, 200 insertions(+), 1 deletion(-) create mode 100644 Tensorflow/tf_logs/run-20180829032206/events.out.tfevents.1535512938.LordSomen create mode 100644 logs/relu1/events.out.tfevents.1535517249.LordSomen create mode 100644 logs/relu2/events.out.tfevents.1535517285.LordSomen create mode 100644 logs/relu6/events.out.tfevents.1535524640.LordSomen create mode 100644 logs/relu8/events.out.tfevents.1535524643.LordSomen create mode 100644 logs/relu9/events.out.tfevents.1535524650.LordSomen create mode 100644 tf.logs/run-20180829032123/events.out.tfevents.1535512895.LordSomen diff --git a/Tensorflow/tf.py b/Tensorflow/tf.py index 8b3fb12..0b8a6de 100644 --- a/Tensorflow/tf.py +++ b/Tensorflow/tf.py @@ -375,4 +375,203 @@ def fetch_batch(epoch, batch_index, batch_size): #%% print(error.op.name) -print(mse.op.name) \ No newline at end of file +print(mse.op.name) + +#%% +''' modularity ''' +n_features = 3 +X = tf.placeholder(tf.float32,shape=(None,n_features), +name="X") +w1 = tf.Variable(tf.random_normal((n_features,1)), +name="weights1") +w2 = tf.Variable(tf.random_normal((n_features,1)), +name="weights2") +b1 = tf.Variable(0.0,name="bias1") +b2 = tf.Variable(0.0,name="bias2") +z1 = tf.add(tf.matmul(X,w1),b1,name="z1") +z2 = tf.add(tf.matmul(X,w2),b2,name="z2") + +relu1 = tf.maximum(z1,0.,name="relu1") +relu2 = tf.maximum(z2,0.,name="relu2") + +output = tf.add(relu1,relu2,name="output") + +print(output) + +#%% +reset_graph() + +def relu(X): + w_shape = (int(X.get_shape()[1]),1) + w = tf.Variable(tf.random_normal(w_shape), + name="weights") + b = tf.Variable(0.0,name="bias") + z = tf.add(tf.matmul(X,w),b,name="z") + return tf.maximum(z,0.,name="relu") + +n_features = 3 +X = tf.placeholder(tf.float32,shape=(None,n_features) +,name="X") +relus = [relu(X) for i in range(5)] +output = tf.add_n(relus,name="output") +print(output) +#%% +file_writer = tf.summary.FileWriter("logs/relu1", + tf.get_default_graph()) + +#%% +reset_graph() + +def relu_namescope(X): + with tf.name_scope("relu"): + w_shape = (int(X.get_shape()[1]),1) + w = tf.Variable(tf.random_normal(w_shape), + name="weights") + b = tf.Variable(0.0,name="bias") + z = tf.add(tf.matmul(X,w),b,name="z") + return tf.maximum(z,0.,name="max") + +#%% +n_features = 3 +X = tf.placeholder(tf.float32, shape=(None, n_features), + name="X") +relus = [relu_namescope(X) for i in range(5)] +output = tf.add_n(relus, name="output") + +file_writer = tf.summary.FileWriter("logs/relu2", + tf.get_default_graph()) +file_writer.close() + +#%% +''' shared variable ''' +reset_graph() + +def relu_threshold(X,threshold): + with tf.name_scope("relu"): + w_shape = (int(X.get_shape()[1]),1) + w = tf.Variable(tf.random_normal(w_shape), + name="weights") + b = tf.Variable(0.0,name="bias") + z = tf.add(tf.matmul(X,w),b,name="z") + return tf.maximum(z,threshold,name="max") + +threshold = tf.Variable(0.0 , name="threshold") +X = tf.placeholder(tf.float32,shape=(None,n_features), +name="X") +relus = [relu_threshold(X,threshold) for i in range(5)] +output = tf.add_n(relus,name="output") +print(output) + +#%% + +reset_graph() +def relu(X): + with tf.name_scope("relu"): + if not hasattr(relu, "threshold"): + relu.threshold = tf.Variable(0.0, + name="threshold") + w_shape = int(X.get_shape()[1]), 1 + w = tf.Variable(tf.random_normal(w_shape), + name="weights") + b = tf.Variable(0.0, name="bias") + z = tf.add(tf.matmul(X, w), b, name="z") + return tf.maximum(z, relu.threshold, name="max") + +#%% +X = tf.placeholder(tf.float32, shape=(None, n_features), + name="X") +relus = [relu(X) for i in range(5)] +output = tf.add_n(relus, name="output") +print(output) +#%% +reset_graph() + +with tf.variable_scope("relu"): + threshold = tf.get_variable("threshold", shape=(), + initializer=tf.constant_initializer(0.0)) + +#%% +with tf.variable_scope("relu", reuse=True): + threshold = tf.get_variable("threshold") + +#%% +with tf.variable_scope("relu") as scope: + scope.reuse_variables() + threshold = tf.get_variable("threshold") + +#%% +reset_graph() + +def relu(X): + with tf.variable_scope("relu", reuse=True): + threshold = tf.get_variable("threshold") + w_shape = int(X.get_shape()[1]), 1 + w = tf.Variable(tf.random_normal(w_shape), + name="weights") + b = tf.Variable(0.0, name="bias") + z = tf.add(tf.matmul(X, w), b, name="z") + return tf.maximum(z, threshold, name="max") + +X = tf.placeholder(tf.float32, shape=(None, n_features), + name="X") +with tf.variable_scope("relu"): + threshold = tf.get_variable("threshold", shape=(), + initializer=tf.constant_initializer(0.0)) +relus = [relu(X) for relu_index in range(5)] +output = tf.add_n(relus, name="output") +print(output) + +#%% +file_writer = tf.summary.FileWriter("logs/relu6", + tf.get_default_graph()) +file_writer.close() + +#%% +reset_graph() + +def relu(X): + with tf.variable_scope("relu"): + threshold = tf.get_variable("threshold", shape=(), initializer=tf.constant_initializer(0.0)) + w_shape = (int(X.get_shape()[1]), 1) + w = tf.Variable(tf.random_normal(w_shape), + name="weights") + b = tf.Variable(0.0, name="bias") + z = tf.add(tf.matmul(X, w), b, name="z") + return tf.maximum(z, threshold, name="max") + +X = tf.placeholder(tf.float32, shape=(None, n_features), + name="X") +with tf.variable_scope("", default_name="") as scope: + first_relu = relu(X) # create the shared variable + scope.reuse_variables() # then reuse it + relus = [first_relu] + [relu(X) for i in range(4)] +output = tf.add_n(relus, name="output") + +file_writer = tf.summary.FileWriter("logs/relu8", + tf.get_default_graph()) +file_writer.close() + +#%% +reset_graph() + +def relu(X): + threshold = tf.get_variable("threshold", shape=(), + initializer=tf.constant_initializer(0.0)) + w_shape = (int(X.get_shape()[1]), 1) + w = tf.Variable(tf.random_normal(w_shape), + name="weights") + b = tf.Variable(0.0, name="bias") + z = tf.add(tf.matmul(X, w), b, name="z") + return tf.maximum(z, threshold, name="max") + +X = tf.placeholder(tf.float32, shape=(None, n_features), + name="X") +relus = [] +for relu_index in range(5): + with tf.variable_scope("relu",reuse=(relu_index >= 1)) as scope: + relus.append(relu(X)) +output = tf.add_n(relus, name="output") + +#%% +file_writer = tf.summary.FileWriter("logs/relu9", 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