InvalidArgumentError：您必须使用dtype float输入占位符张量'Placeholder_109'的值

Question

I am using Tensorflow to feed my data. But I got this error, I have provided both code and error in blow. My datase is 400 gray scale and 16x16 image size.

Here it is my_dataset() function

 def my_dataset(): print ('HELLO') # fully connected variables resulting_width = image_width // (max_pool_size1 * max_pool_size2) resulting_height = image_height // (max_pool_size1 * max_pool_size2) full1_input_size = resulting_width * resulting_height*conv2_features full1_weight = tf.Variable(tf.truncated_normal([full1_input_size, fully_connected_size1], stddev=0.1, dtype=tf.float32)) full1_bias = tf.Variable(tf.truncated_normal([fully_connected_size1], stddev=0.1, dtype=tf.float32)) full2_weight = tf.Variable(tf.truncated_normal([fully_connected_size1, target_sizeconvert], stddev=0.1, dtype=tf.float32)) full2_bias = tf.Variable(tf.truncated_normal([target_sizeconvert], stddev=0.1, dtype=tf.float32)) # Dropout placeholder dropout = tf.placeholder(tf.float32, shape=()) print("cheking5") #%% #shape of input = [batch, in_height, in_width, in_channels] #shape of filter = [filter_height, filter_width, in_channels, out_channels] # Initialize Model Operations def my_conv_net(input_data): ########### First Conv-ReLU-MaxPool Layer ########### conv1 = tf.nn.conv2d(input_data, conv1_weight, strides=[1, 1, 1, 1], padding='SAME') relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_bias)) max_pool1 = tf.nn.max_pool(relu1, ksize=[1, max_pool_size1, max_pool_size1, 1], strides=[1, max_pool_size1, max_pool_size1, 1], padding='SAME') print("input_data>>>: ", input_data) print("conv1_weight>>>: ", conv1_weight) print("conv1_weight get shape: ", conv1_weight.get_shape()) print("conv1_bias>>>: ", conv1_bias) print("conv1>>>: ", conv1) print("relu1>>>: ", relu1) print("max_pool1>>>: ", max_pool1) print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\\n") # Second Conv-ReLU-MaxPool Layer conv2 = tf.nn.conv2d(max_pool1, conv2_weight, strides=[1, 1, 1, 1], padding='SAME') relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_bias)) max_pool2 = tf.nn.max_pool(relu2, ksize=[1, max_pool_size2, max_pool_size2, 1], strides=[1, max_pool_size2, max_pool_size2, 1], padding='SAME') print("conv2_weight>>>: ", conv2_weight) print("conv2_bias>>>: ", conv2_bias) print("conv2>>>: ", conv2) print("relu2>>>: ", relu2) print("max_pool2>>>: ", max_pool2) print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\\n") # Transform Output into a 1xN layer for next fully connected layer final_conv_shape = max_pool2.get_shape().as_list() final_shape = final_conv_shape[1] * final_conv_shape[2] * final_conv_shape[3] flat_output = tf.reshape(max_pool2, [final_conv_shape[0], final_shape]) # First Fully Connected Layer print("flat_output ***: ", type(flat_output)) print("flat_output ***: ", flat_output) print("full1_weight ***: ", type(full1_weight)) print("full1_weight ***: ", full1_weight) print("full1_bias ***: ", type(full1_bias)) print("full1_bias ***: ", full1_bias) print("$$$$$$$$$$$$$$$$ checking2 $$$$$$$$$$$$$$$$$$$\\n") fully_connected1 = tf.nn.relu(tf.add(tf.matmul(flat_output, full1_weight), full1_bias)) # Second Fully Connected Layer final_model = tf.add(tf.matmul(fully_connected1, full2_weight), full2_bias) # Add dropout final_model_output = tf.nn.dropout(final_model, dropout) return(final_model_output) print("checking6\\n") # We can declare the model on the training and test data model_output = my_conv_net(x_input) test_model_output = my_conv_net(eval_input) print("YYYYYYy_target***: ", y_target) print("XXXXXx_input***: ", x_input) # Declare Loss Function (softmax cross entropy) loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=model_output, labels=y_target)) print("checking7") # Create a prediction function prediction = tf.nn.softmax(model_output) test_prediction = tf.nn.softmax(test_model_output) print("checking8") # Create accuracy function def get_accuracy(logits, targets): batch_predictions = np.argmax(logits, axis=1) num_correct = np.sum(np.equal(batch_predictions, targets)) return(100. * num_correct/batch_predictions.shape[0]) # Create an optimizer my_optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9) train_step = my_optimizer.minimize(loss) # Initialize Variables #init = tf.global_variables_initializer() init=tf.group(tf.global_variables_initializer(),tf.local_variables_initializer()) sess = tf.Session() sess.run(init) print("checking9") # Start training loop train_loss = [] train_acc = [] test_acc = [] for i in range(generations): rand_index = np.random.choice(len(ttrainData), size=batch_size) print("Random Index",rand_index) rand_x = ttrainData[rand_index] rand_x = np.expand_dims(rand_x, 3) rand_y = ttrainLabels[rand_index] print("TRAIN LABEL",ttrainLabels.dtype," dtype",ttrainLabels[0].dtype) #rand_y = np.array(rand_y,dtype=np.uint8) train_dict = {x_input: rand_x, y_target: rand_y} print("Dictionary",train_dict) print("\\nKJKJKJKJKJ\\n") # print("train_dict***: ", train_dict) print("train_step***: ", train_step) print("train_step***: ", type(train_step)) print("\\nrand_index***: ", rand_index.dtype) print("Type of rand_index***: ", type(rand_index)) print("Length of the rand_index: ", len(rand_index)) print("\\nrand_x***: ", rand_x.dtype) print("rand_x***:", type(rand_x)) print("rand_x[0]***:", type(rand_x[0])) print("Length of rand_x: ", len(rand_x)) # print("Convert rand_x to float32: ", rand_x.astype(np.float32)) print("rand_x[0]***:", type(rand_x[0])) print("rand_x[0]***: ", rand_x[0].dtype) print("\\nrand_y***: ", rand_y.dtype) print("rand_y***:",type(rand_y)) print("rand_y[0]***:", type(rand_y[0])) print("Convert rand_y to Uint8: ", rand_y[0].astype(int)) print("rand_y[0]***:", type(rand_y[0])) print("Length of rand_y: ", len(rand_y)) sess.run(train_step, feed_dict = train_dict) print(sess1.run(loss), feed_dict={xs:rand_x, ys:rand_y}) temp_train_loss, temp_train_preds = sess.run([loss, prediction], feed_dict = train_dict) temp_train_acc = get_accuracy(temp_train_preds, rand_y) if (i+1) % eval_every == 0: eval_index = np.random.choice(len(ttestData), size=evaluation_size) eval_x = ttestData[eval_index] eval_x = np.expand_dims(eval_x, 3) eval_y = ttestLabels[eval_index] test_dict = {eval_input: eval_x, eval_target: eval_y} test_preds = sess.run(test_prediction, feed_dict=test_dict) temp_test_acc = get_accuracy(test_preds, eval_y) # Record and print results train_loss.append(temp_train_loss) train_acc.append(temp_train_acc) test_acc.append(temp_test_acc) acc_and_loss = [(i+1), temp_train_loss, temp_train_acc, temp_test_acc] acc_and_loss = [np.round(x,2) for x in acc_and_loss] print('Generation # {}. Train Loss: {:.2f}. Train Acc (Test Acc): {:.2f} ({:.2f})'.format(*acc_and_loss)) print("checking10") 

And it gives me this error.

 runfile('C:/Users/Tala/Desktop/KSU/GRA-Thesis/Poster-Spring17/CNN-Xray/Xray-TF/CNN-Xray-TF-CookBook.py', wdir='C:/Users/Tala/Desktop/KSU/GRA-Thesis/Poster-Spring17/CNN-Xray/Xray-TF') Reloaded modules: Mydataset_Xray HELLO First path of image: C:/Users/Tala/Desktop/KSU/GRA-Thesis/Poster-Spring17/CNN-Xray/Xray-TF/Xrays-01-resized/sample_0_111.jpg cheking5 checking6 input_data>>>: Tensor("Placeholder:0", shape=(100, 16, 16, 1), dtype=float32) conv1_weight>>>: <tf.Variable 'Variable_66:0' shape=(4, 4, 1, 32) dtype=float32_ref> conv1_weight get shape: (4, 4, 1, 32) conv1_bias>>>: <tf.Variable 'Variable_67:0' shape=(32,) dtype=float32_ref> conv1>>>: Tensor("Conv2D_20:0", shape=(100, 16, 16, 32), dtype=float32) relu1>>>: Tensor("Relu_32:0", shape=(100, 16, 16, 32), dtype=float32) max_pool1>>>: Tensor("MaxPool_20:0", shape=(100, 8, 8, 32), dtype=float32) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ conv2_weight>>>: <tf.Variable 'Variable_68:0' shape=(4, 4, 32, 64) dtype=float32_ref> conv2_bias>>>: <tf.Variable 'Variable_69:0' shape=(64,) dtype=float32_ref> conv2>>>: Tensor("Conv2D_21:0", shape=(100, 8, 8, 64), dtype=float32) relu2>>>: Tensor("Relu_33:0", shape=(100, 8, 8, 64), dtype=float32) max_pool2>>>: Tensor("MaxPool_21:0", shape=(100, 4, 4, 64), dtype=float32) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ flat_output ***: <class 'tensorflow.python.framework.ops.Tensor'> flat_output ***: Tensor("Reshape_15:0", shape=(100, 1024), dtype=float32) full1_weight ***: <class 'tensorflow.python.ops.variables.Variable'> full1_weight ***: <tf.Variable 'Variable_70:0' shape=(1024, 100) dtype=float32_ref> full1_bias ***: <class 'tensorflow.python.ops.variables.Variable'> full1_bias ***: <tf.Variable 'Variable_71:0' shape=(100,) dtype=float32_ref> $$$$$$$$$$$$$$$$ checking2 $$$$$$$$$$$$$$$$$$$ input_data>>>: Tensor("Placeholder_42:0", shape=(500, 16, 16, 1), dtype=float32) conv1_weight>>>: <tf.Variable 'Variable_66:0' shape=(4, 4, 1, 32) dtype=float32_ref> conv1_weight get shape: (4, 4, 1, 32) conv1_bias>>>: <tf.Variable 'Variable_67:0' shape=(32,) dtype=float32_ref> conv1>>>: Tensor("Conv2D_22:0", shape=(500, 16, 16, 32), dtype=float32) relu1>>>: Tensor("Relu_35:0", shape=(500, 16, 16, 32), dtype=float32) max_pool1>>>: Tensor("MaxPool_22:0", shape=(500, 8, 8, 32), dtype=float32) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ conv2_weight>>>: <tf.Variable 'Variable_68:0' shape=(4, 4, 32, 64) dtype=float32_ref> conv2_bias>>>: <tf.Variable 'Variable_69:0' shape=(64,) dtype=float32_ref> conv2>>>: Tensor("Conv2D_23:0", shape=(500, 8, 8, 64), dtype=float32) relu2>>>: Tensor("Relu_36:0", shape=(500, 8, 8, 64), dtype=float32) max_pool2>>>: Tensor("MaxPool_23:0", shape=(500, 4, 4, 64), dtype=float32) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ flat_output ***: <class 'tensorflow.python.framework.ops.Tensor'> flat_output ***: Tensor("Reshape_16:0", shape=(500, 1024), dtype=float32) full1_weight ***: <class 'tensorflow.python.ops.variables.Variable'> full1_weight ***: <tf.Variable 'Variable_70:0' shape=(1024, 100) dtype=float32_ref> full1_bias ***: <class 'tensorflow.python.ops.variables.Variable'> full1_bias ***: <tf.Variable 'Variable_71:0' shape=(100,) dtype=float32_ref> $$$$$$$$$$$$$$$$ checking2 $$$$$$$$$$$$$$$$$$$ YYYYYYy_target***: Tensor("Placeholder_41:0", shape=(100,), dtype=int32) XXXXXx_input***: Tensor("Placeholder:0", shape=(100, 16, 16, 1), dtype=float32) checking7 checking8 checking9 Random Index [118 269 164 ..., 15 10 170] TRAIN LABEL uint8 dtype uint8 KJKJKJKJKJ train_step***: name: "Momentum_4" op: "NoOp" input: "^Momentum_4/update_Variable_66/ApplyMomentum" input: "^Momentum_4/update_Variable_67/ApplyMomentum" input: "^Momentum_4/update_Variable_68/ApplyMomentum" input: "^Momentum_4/update_Variable_69/ApplyMomentum" input: "^Momentum_4/update_Variable_70/ApplyMomentum" input: "^Momentum_4/update_Variable_71/ApplyMomentum" input: "^Momentum_4/update_Variable_72/ApplyMomentum" input: "^Momentum_4/update_Variable_73/ApplyMomentum" train_step***: <class 'tensorflow.python.framework.ops.Operation'> rand_index***: int32 Type of rand_index***: <class 'numpy.ndarray'> Length of the rand_index: 100 y_target***: Tensor("Placeholder_41:0", shape=(100,), dtype=int32) y_target***: <class 'tensorflow.python.framework.ops.Tensor'> x_input***: Tensor("Placeholder:0", shape=(100, 16, 16, 1), dtype=float32) x_input***: <class 'tensorflow.python.framework.ops.Tensor'> rand_x***: float32 rand_x***: <class 'numpy.ndarray'> rand_x[0]***: <class 'numpy.ndarray'> Length of rand_x: 100 rand_x[0]***: <class 'numpy.ndarray'> rand_x[0]***: float32 rand_y***: uint8 rand_y***: <class 'numpy.ndarray'> rand_y[0]***: <class 'numpy.uint8'> Convert rand_y to Uint8: 1 rand_y[0]***: <class 'numpy.uint8'> Length of rand_y: 100 Traceback (most recent call last): File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py", line 866, in runfile execfile(filename, namespace) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py", line 102, in execfile exec(compile(f.read(), filename, 'exec'), namespace) File "C:/Users/Tala/Desktop/KSU/GRA-Thesis/Poster-Spring17/CNN-Xray/Xray-TF/CNN-Xray-TF-CookBook.py", line 265, in <module> sess.run(train_step, feed_dict={x_input: rand_x, y_target: rand_y}) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tensorflow\\python\\client\\session.py", line 789, in run run_metadata_ptr) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tensorflow\\python\\client\\session.py", line 997, in _run feed_dict_string, options, run_metadata) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tensorflow\\python\\client\\session.py", line 1132, in _do_run target_list, options, run_metadata) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tensorflow\\python\\client\\session.py", line 1152, in _do_call raise type(e)(node_def, op, message) InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_44' with dtype float [[Node: Placeholder_44 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] Caused by op 'Placeholder_44', defined at: File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\spyder\\utils\\ipython\\start_kernel.py", line 227, in <module> main() File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\spyder\\utils\\ipython\\start_kernel.py", line 223, in main kernel.start() File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\ipykernel\\kernelapp.py", line 474, in start ioloop.IOLoop.instance().start() File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\zmq\\eventloop\\ioloop.py", line 177, in start super(ZMQIOLoop, self).start() File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tornado\\ioloop.py", line 887, in start handler_func(fd_obj, events) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tornado\\stack_context.py", line 275, in null_wrapper return fn(*args, **kwargs) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py", line 440, in _handle_events self._handle_recv() File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py", line 472, in _handle_recv self._run_callback(callback, msg) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py", line 414, in _run_callback callback(*args, **kwargs) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tornado\\stack_context.py", line 275, in null_wrapper return fn(*args, **kwargs) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\ipykernel\\kernelbase.py", line 276, in dispatcher return self.dispatch_shell(stream, msg) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\ipykernel\\kernelbase.py", line 228, in dispatch_shell handler(stream, idents, msg) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\ipykernel\\kernelbase.py", line 390, in execute_request user_expressions, allow_stdin) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\ipykernel\\ipkernel.py", line 196, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\ipykernel\\zmqshell.py", line 501, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2717, in run_cell interactivity=interactivity, compiler=compiler, result=result) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2827, in run_ast_nodes if self.run_code(code, result): File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2881, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-28-1db506e814de>", line 1, in <module> runfile('C:/Users/Tala/Desktop/KSU/GRA-Thesis/Poster-Spring17/CNN-Xray/Xray-TF/CNN-Xray-TF-CookBook.py', wdir='C:/Users/Tala/Desktop/KSU/GRA-Thesis/Poster-Spring17/CNN-Xray/Xray-TF') File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py", line 866, in runfile execfile(filename, namespace) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py", line 102, in execfile exec(compile(f.read(), filename, 'exec'), namespace) File "C:/Users/Tala/Desktop/KSU/GRA-Thesis/Poster-Spring17/CNN-Xray/Xray-TF/CNN-Xray-TF-CookBook.py", line 111, in <module> dropout = tf.placeholder(tf.float32, shape=()) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py", line 1530, in placeholder return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tensorflow\\python\\ops\\gen_array_ops.py", line 1954, in _placeholder name=name) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py", line 767, in apply_op op_def=op_def) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py", line 2506, in create_op original_op=self._default_original_op, op_def=op_def) File "C:\\Program Files\\Anaconda2\\envs\\py3.5\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py", line 1269, in __init__ self._traceback = _extract_stack() InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_44' with dtype float [[Node: Placeholder_44 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

I really don't know where is the problem. I change dtype of each parameter to float32, but still it gives me the sam eerror.

Any help would be appreciate

Answer 1

I found out what the problem is. I created the placeholder for dropout but I didn't add it to the train_dic and test_dic. So it keeps asking to fill this placeholder.

train_dict = {x_input: rand_x, y_target: rand_y, dropout: dropout_prob}

test_dict = {eval_input: eval_x, eval_target: eval_y, dropout: 1.0}